TYPE I INTERFERON SCORE IS ASSOCIATED WITH THE SEVERITY AND POOR PROGNOSIS IN ANTI-MDA5 ANTIBODY-POSITIVE DERMATOMYOSITIS PATIENTS

BackgroundAnti-MDA5 antibody-positive dermatomyositis (anti-MDA5+DM) is a rare autoimmune disease associated with a high mortality rate due to rapid-progressive interstitial lung disease (RP-ILD), particularly in East Asia[1]. MDA5, acts as a cytoplasmic sensor of viral RNA, thus activating antiviral responses including the type I interferon (IFN) signaling pathway[2]. The involvement of type 1 IFN in the pathogenesis of MDA5+DM has been proposed based on the significantly elevated expression of its downstream stimulated genes(ISG) in muscle, skin, lung, and peripheral blood[3;4]. Janus kinase inhibitor, which targets the IFN pathway, combined with glucocorticoid could improve the survival of early-stage MDA5+DM-ILD patients[5]. In clinical practice, there is still an urgent demand for sensitive biomarkers to facilitate clinical risk assessment and precise treatment.ObjectivesThis study aimed to investigate the clinical significance of interferon score, especially IFN-I score, in patients with anti-MDA5+DM.MethodsDifferent subtypes of idiopathic inflammatory myopathy, including anti-MDA5+DM(n=61), anti-MDA5-DM(n=20), antisynthetase syndrome(ASS,n=22),polymyositis(PM,n=6) and immune-mediated necrotizing myopathy(IMNM,n=9), and 58 healthy controls were enrolled.. A multiplex quantitative real-time PCR(RT-qPCR) assay using four TaqMan probes was utilized to evaluate two type I ISGs (IFI44, MX1, which are used for IFN-I score), one type II ISG (IRF1), and one housekeeping gene (HRPT1). Clinical features and disease activity index were compared between high and low IFN-I score groups in 61 anti-MDA5+DM patients. The association between laboratory findings and the predictive value of baseline IFN-I score level for mortality was analyzed.ResultsThe IFN scores were significantly higher in patients with anti-MDA5+DM than in HC (Figure 1A). The IFN-I score correlated positively with serum IFN α(r = 0.335, P =0.008), ferritin (r = 0.302, P = 0.018), and Myositis Disease Activity Assessment Visual Analogue Scale (MYOACT) score(r=0.426, P=0.001). Compared with patients with low IFN-I scores, patients with high IFN-I scores showed increased MYOACT score, CRP, AST, ferritin, and the percentages of plasma cells (PC%) but decreased lymphocyte count, natural killer cell count, and monocyte count. The 3-month survival rate was significantly lower in patients with IFN-I score > 4.9 than in those with IFN-I score ≤ 4.9(72.9% vs. 100%, P=0.044)(Figure 1B).ConclusionIFN score, especially IFN-I score, detected by multiplex RT-qPCR, can be a valuable biomarker for monitoring disease activity and predicting mortality in anti-MDA5+DM patients.References[1]I.E. Lundberg, M. Fujimoto, J. Vencovsky, R. Aggarwal, M. Holmqvist, L. Christopher-Stine, A.L. Mammen, and F.W. Miller, Idiopathic inflammatory myopathies. Nat Rev Dis Primers 7 (2021) 86.[2]G. Liu, J.H. Lee, Z.M. Parker, D. Acharya, J.J. Chiang, M. van Gent, W. Riedl, M.E. Davis-Gardner, E. Wies, C. Chiang, and M.U. Gack, ISG15-dependent activation of the sensor MDA5 is antagonized by the SARS-CoV-2 papain-like protease to evade host innate immunity. Nat Microbiol 6 (2021) 467-478.[3]G.M. Moneta, D. Pires Marafon, E. Marasco, S. Rosina, M. Verardo, C. Fiorillo, C. Minetti, L. Bracci-Laudiero, A. Ravelli, F. De Benedetti, and R. Nicolai, Muscle Expression of Type I and Type II Interferons Is Increased in Juvenile Dermatomyositis and Related to Clinical and Histologic Features. Arthritis Rheumatol 71 (2019) 1011-1021.[4]Y. Ye, Z. Chen, S. Jiang, F. Jia, T. Li, X. Lu, J. Xue, X. Lian, J. Ma, P. Hao, L. Lu, S. Ye, N. Shen, C. Bao, Q. Fu, and X. Zhang, Single-cell profiling reveals distinct adaptive immune hallmarks in MDA5+ dermatomyositis with therapeutic implications. Nat Commun 13 (2022) 6458.[5]Z. Chen, X. Wang, and S. Ye, Tofacitinib in Amyopathic Dermatomyositis–Associated Interstitial Lung Disease. New England Journal of Medicine 381 (2019) 291-293.AcknowledgementsThis work was supported by the National Natural Science Foundation of China [81974251], and Shanghai Hospital Develop ent Center, Joint Research of New Advanced Technology Project [SHDC12018106]Disclosure of InterestsNone Declared.

Drug discovery for coronavirus disease 2019 based on text mining and biomedical databases.

Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway.Copyright © 2022 Tianjin Press of Chinese Herbal Medicines. All Rights Reserved.

Cohort study on the change trend of astigmatism among preschool children in Baiyun district, Guangzhou from 2018 to 2021.

AIM: To explore the characteristics of astigmatism in preschool children before, during and after the COVID-19 epidemic, so as to provide a reference for further prevention and control of children's vision. *METHODS: In the consecutive four years from January 2018 to December 2021, a retrospective analysis of vision data was conducted on 2 273 preschool children (4546 eyes) younger than 4 years old who participated in children' s vision screening test in Baiyun district, Guangzhou. They were divided into 1 - year old group (ages<1-year old, 420 cases), 2-year old group (1-year <= ages < 2- year, 543 cases), 3- year old group (2- year <=ages <3-year, 614 cases), and 4 - year old group (3 - year <= ages< 4- year, 696 cases) according to ages. The analysis included astigmatic degrees of children's eyes as well as their conditions of astigmatism. *RESULTS: In 2018, the astigmatic degrees of the both eyes of 1-year-old group were higher than those of other groups (P<0.05). The binocular astigmatic degrees of the preschool children in four groups were obviously higher in 2020 than 2019 (P < 0. 05), while they were significantly decreased in 2021 when compared with 2020 (P < 0. 05). From 2019 to 2020, the increase of astigmatic degrees of the right eye is more considerable than the left eye of preschool children in those four groups (P < 0. 001). Furthermore, the morbidity of astigmatism basically echoes with the changing tendency of astigmatic degrees from 2018 to 2021. *CONCLUSIONS: Preschool children in Baiyun district, Guangzhou, have the highest degree of astigmatism and the fastest progression rate within 1 year old. Before COVID - 19 epidemic, the changes in astigmatism and prevalence were relatively stable;during COVID - 19 epidemic, the astigmatism and prevalence increased significantly and the astigmatic degrees of right eye increased more than that of the left eye;after the normalization of epidemic prevention and control, the astigmatism and prevalence decreased significantly.Copyright © 2023 International Journal of Ophthalmology (c/o Editorial Office). All rights reserved.

A Literature Review on the Antiviral Mechanism of Luteolin

Background: Viral infections pose some of the most serious human health concerns worldwide. The infections caused by several viruses, including coronavirus, hepatitis virus, and human immunodeficiency virus, are difficult to treat. Method(s): This review details the findings of a literature search performed on the antiviral properties of luteolin. The keywords engaged in the search are "virus" along with "luteolin." Results: Luteolin possesses antiviral properties, which is the basis for the current review. It is an important natural flavonoid with numerous important biological properties, including anti-inflammatory, immune regulatory, and antitumor effects, and is found in vegetables, fruits, and several medicinal plants. Recent studies have revealed that many traditional Chinese medicines that contain luteolin inhibit the replication of coronaviruses. Conclusion(s): Luteolin effectively inhibits the replication of coronavirus, influenza virus, enterovirus, rotavirus, herpes virus, and respiratory syncytial virus, among others. In particular, it prevents viral infection by improving the body's nonspecific immunity and antioxidation capacity and inhibiting many pathways related to virus infection and replication, such as MAPK, PI3K-AKT, TLR4/8, NF-kappaB, Nrf-2/hemeoxygenase-1, and others. It also regulates the expression of some receptors and factors, including hepatocyte nuclear factor 4alpha, p53, NLRP3, TNF-alpha, and interleukins, thereby interfering with the replication of viruses in cells. Luteolin also promotes the repair of damaged cells induced by proinflammatory factors by regulating the expression of inflammatory molecules. The overall effect of these processes is the reduction in viral replication and, consequently, the viral load. This review summarizes the antiviral effect of luteolin and the mechanism underlying this property.Copyright © The Author(s) 2023.

A Novel Scheme Based on Variational Mode Decomposition and Multiple Denoising Technologies for Heartbeat Rate Estimation

In the face of the serious aging of the global population and the sudden outbreak of COVID-19, monitoring human vital signs such as heart rate is very important to save lives. For more accurate heartbeat detection, we propose a heartbeat detection scheme based on variational mode decomposition (VMD) and multiple technologies of noise and interference suppression. First, a filter is designed to suppress the impulse noise and reduce the loss of useful signal information. Then, VMD is performed to decompose the pre-processed vital signs into a series of intrinsic mode function (IMF) components. Thirdly, much attention is paid on denoising of IMF components corresponding to the heartbeat signals, an improved wavelet threshold denoising method is proposed to process these IMF components and reconstruct the heartbeat signal. Finally, an adaptive notch filter is used to process the residual respiratory harmonics in the reconstructed heartbeat signal. To verify the heartbeat detection accuracy of our method, the results are compared with a reliable reference sensor. Our results show that the mean average absolute error (AAE) of heart rate estimated by the proposed method is 1.06 bpm, which is 7.51 bpm better than the original method. © 2022 IEEE.

Path Planning Algorithms for Mobile Robots in Hospital Environment during Covid-19

Since 2019, the COVID-19 virus has spread worldwide, posing a significant health and safety concern. The application of mobile robots in the medical field has gradually demonstrated their unique advantages. Therefore, we focus on the application of mobile robots inwards. By collating and summarizing some of the most popular existing path planning algorithms, this paper illustrates that different algorithms can produce varying outcomes depending on different environments and hardware used. MATLAB is used in this study to simulate four algorithms: To determine the most efficient path, A∗, RRT, RRT∗, and PRM in a specific hospital map are compared, as well as parameters including path length, average execution time, and resource consumption. Modelling a single-layer hospital map makes it possible for mobile robots in the medical field to execute tasks more efficiently between entry and ward in the COVID-19 hospital environment. Based on a comparison and comprehensive consideration of the data derived from the simulations, it is found that the A∗algorithm is superior in terms of optimality, completeness, time complexity, and spatial complexity. Therefore, the A∗algorithm is more valuable in finding the best path for a mobile robot in a hospital environment. © 2022 ACM.

Study on the mechanism of container port congestion under the epidemic

First, this paper analyzes the congestion of container ports at home and abroad under the current epidemic situation, then takes Yantian port of Shenzhen as the key research object to analyze the waiting time at anchorage and the stopping time at berth of the main container ports in China based on the data statistics. It studies the anchorage demand of ships with random and fluctuating arrival based on queuing theory. It gives the relationship between the stopping time of ship at berth and the maximum waiting time of the arriving ship, and reveals that the increase of ship stopping time and uneven arrival at the port are the main factors causing the current container port congestion, and puts forward some countermeasures to alleviate the port congestion. © 2022 SPIE

YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

Wearing of surgical face masks has become the new norm of our daily life in the context of the COVID-19 pandemic. Under many conditions at various public places, it is necessary to check or monitor whether the face mask is worn properly. Manual judgement of mask wearing not only wastes manpower but also fails to monitor it in a way of all-time and real-time, posing the urge of an automatic mask wearing detection technology. Earlier automatic mask wearing methods uses a successive means in which the face is detected first and then the mask is determined and judged followingly. More recent methods take the end-to-end paradigm by utilizing successful and well-known CNN models from the field of object detection. However, these methods fail to consider the diversity of face mask wearing, such as different kinds of irregularity and spoofing. Thus, we in this study introduce a comprehensive mask wearing detection dataset (named as Diverse Masked Faces) by distinguishing a total of five different classes of mask wearing. We then adapt the YOLOX model for our specific task and further improve it using a new composite loss which merges the CIoU and the alpha-IoU losses and inherits both their advantages. The improved model is referred as YoloMask. Our proposed method was tested on the new dataset and has been proved to significantly outperform other SOTA methods in the literature that are either successive or end-to-end. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Low Serum Albumin at Admission Is Associated with Mortality among Inpatients with Cirrhosis: A Multi-National Experience

Background: MELD and Child-Pugh scores have traditionally been used as prognostic indicators in patients with cirrhosis. Albumin infusions in outpatients have been associated with improved outcomes, but not in transplant waitlisted patients or inpatients. This aim of this study was to assess whether low serum albumin (sAlb) on admission alone is a poor prognostic indicator among cirrhotic inpatients from a new multi-national cohort. Method(s): The CLEARED study is a global study that enrolled consecutive non-electively admitted inpatients without organ transplant or COVID-19 from 6 continents. Admission demographics, medical history, laboratory data, inpatient course, death/hospice transfer and mortality at 30 days post-discharge were recorded. Patients were divided into 3 groups: sAlb <28gm/L(A), sAlb >=28 but <35gm/L (B), and sAlb>=35gm/L (C) were compared. Multi-variable logistic regression was performed using inpatient mortality and overall 30-day mortality as outcomes. Result(s): 2429 patients were enrolled from 21 countries worldwide. The distribution was A:49%, B:39%, C:12%. Gp A patients were significantly younger (54yrs vs. 57yrs vs 58yrs p<0.0001) but with similar gender distribution, and higher MELD-Na score of 25 vs. 20 vs. 17 (p<0.0001). Gp A patients were more likely to have alcohol as etiology of cirrhosis (49% vs. 45% vs 38%, p=0.004), and were more likely to have either infection (27% vs. 18% vs. 13%, p<0.0001), HE (39% vs. 33% vs. 23%, p=0.005) or fluid related issues as a reason for admission (p<0.0001). More patients in Gp A received albumin infusion during their hospital stay (120gm vs. 100gm vs. 100gm p=0.0004), mostly for the indications of AKI (47% vs. 49% vs. 47%, p=0.79) and performance of large volume paracentesis (44% vs. 42% vs. 41%, p=0.80), followed by bacterial peritonitis indication (22% vs. 17% vs. 11%, p=0.01). Group A patients had longer hospital stays (9 days vs. 8 days vs. 7 days (p<0.001), but similar ICU transfer (23% vs. 22% vs. 20%, p=0.55). group A patients were more likely to die while inpatients (19% vs. 11% vs. 5%, p<0.0001), or by 30 days post-discharge (29% vs. 20% vs. 9%, p<0.0001). Table shows the admission variables associated with a poor outcome. Conclusion(s): Hypoalbuminemia is extremely common among admitted cirrhotic patients, with sAlb of <28gm/L occurring in almost half. Together with MELD-Na score and infection at admission, a low sAlb is associated with a poor outcome in these patients. Future studies will need to validate these findings and to assess whether albumin infusions will improve the outcome of these patients. (Figure Presented).

Differences in Mortality Rates in Inpatients with Cirrhosis Depends on the Low, Middle or High Income Country Location: Multi-National Cleared Consortium Experience

Background: Although cirrhosis is a major cause of mortality worldwide, there could be disparities in outcomes. This needs a global consortium to study disparities in inpatient cirrhosis care Aim: Define the impact of location in prediction of outcomes in inpts with cirrhosis. Method(s): CLEARED prospectively enrolled non-electively admitted cirrhosis pts without COVID from all continents. To ensure equity, we allowed only 50 pts/site. Admission details, cirrhosis history, inpatient & 30-day course were recorded. World bank classification of low/low middle income (LMI), upper middle (UMI) & High income (HI) were used. Cirrhosis details, inpatient & 30-day outcomes were compared between groups. Multi-variable regression was performed using inpatient & 30-day mortality as outcomes. Result(s): 2758 pts from 21 countries from all continents, including Africa & Australia, were included.727 were L/LMI, 1050 UMI & 981 pts were from HICs. More men & younger pts were in LMI. Cirrhosis details: More pts in HI gp had 6M hospitalizations & infections, HE & ascites while prior variceal bleeding was higher in LMI . Prior HCC & transplant listings were lower in LMI but similar in UMI/HI. Alcohol & NASH was highest in HI. Viral hepatitis & cryptogenic were highest in UMI.Admissions: Admission MELD was highest in LMI. LMI pts were admitted more for GI Bleed, HE, & DILI, while anasarca & HBV flares were higher in UMI. Higher SBP (36% vs 24% vs 21% p<0.0001) & lowest skin/soft-tissue infections were in LMI (5% vs 5% vs 10% p=0.008);rest were similar. Nosocomial infections, driven by UTI were highest in LMI & HI pts (15% vs 14% vs 11% UMI, p=0.03). Admission diuretics, PPIs, Lactulose & statins were highest & antivirals lower in HI. SBP prophylaxis & rifaximin were highest in LMI pts. Outcome(s): More LMI pts needed ICU & had more organ failures (Fig B). Discharge MELD was highest in LMI. In-hospital mortality was highest & transplant lowest in LMI. This extended to 30-day mortality & transplant in LMI patients vs HI pts.Regression: In-hospital mortality was linked with age, infections, MELD & being in a LMI/UMI vs HIC while being on a transplant list, diabetes, & SBP prophylaxis were protective (Fig C). 30-day mortality predicted by age, ascites, HCC, discharge MELD, organ failures, LMI/UMI vs HIC but rifaximin was protective(Fig D). In-hospital transplant was higher with high MELD, admission rifaximin & listed pts &lower in LMI (OR 0.26) & UMI (OR 0.22) & age. 30-day transplant was higher in those with hyponatremia, ascites & HRS, on the list & on rifaximin and lower in LMI (OR 0.24) & UMI (OR 0.59) vs HI. Conclusion(s): In a global study of inpatients with cirrhosis, there were major differences in outcomes. Not being in a high-income country significantly increased the risk of inpatient and 30-day mortality independent of demographics, medications, in-hospital course, and cirrhosis severity likely due to disparities in access to transplant, which should be accounted for in global models. (Figure Presented).

Risk Propagation Decision-Making for Product and Supply Chain Change Systems Under COVID-19: An Assessment-to-Control Support Scheme

Dynamics in supply chains (SCs) can trigger risks due to the changing and propagating nature. In the context of COVID-19, this article presents an assessment-to-control decision-making support scheme to tackle propagation effect uncertainties of SCs considering product changes. First, a new decision model is proposed for risk warnings, with the potential advantages that: 1) propagation effects can be assessed generally and objectively and 2) permitting control theory to integrate and identify the interrelations between propagation effects. More specifically, the bullwhip effect (BE) with operational and behavioral causes is quantified as cascading amplified inventory fluctuations. The ripple effect (RE) from large-scale supplier disruptions driven by COVID-19 is quantified as increased entropy rates (ERs). Then, the system studied is integrated as a closed-loop control system under provided change control. Moreover, some criteria are derived for the existence of controller gains/decision coefficients to stabilize the closed-loop system with the BE mitigation under the RE. Finally, a mask SC case study under COVID-19 is performed for examining the effectiveness of the proposed scheme. IEEE

Experimental test on the performance of a −80 °C cascade refrigeration unit using refrigerants R290-R170 for COVID-19 vaccines storage

With the urgent demand for ultralow-temperature refrigerators worldwide, the operation reliability and stability of the refrigeration system becomes greatly crucial. In this study, a -80 oC ultralow-temperature cascade refrigeration system (CRS) is developed. From aspects of global warming potential (GWP) and ozone depletion potential (ODP), the environmentally friendly refrigerants R290 and R170 are utilized in the high-temperature cycle and low-temperature cycle of CRS. The experimental measurement is conducted in a Type-laboratory with a dry bulb temperature of 25.0 °C and a wet bulb temperature of 20.2 °C. The pull-down and stable operation performance of the CRS freezer are experimentally investigated. Both the inlet and outlet temperature and pressure of two compressors are monitored, and the operation characteristic of the CRS is analyzed. With some temperature test points arranged in the freezer, the temperature drop and temperature fluctuation variation of air are assessed. The operation power consumption of the CRS is also measured during the whole process. It shows that the −80 °C temperature can be generated and realized by the developed ultralow-temperature freezer. © 2022 Elsevier Ltd

Investigation on contamination of SARS-CoV-2 in cold chain food and key environment in Nantong

Objective To understand the contaminations of SARS-CoV-2 of imported frozen food and corresponding ke;environments in Nantong Farmers’ Wholesale Market. Methods The samples of imported frozen food, surface samples of key environmental objects and throat swabs, anal swabs and blood samples from employees were collected randomly, SARS-CoV-2 nucleic acid testing as well as specific serum antibody IgM and IgG testing were implemented. Results A total of 1 685 sample: of imported frozen food in Nantong, 2 432 samples from key environment, and 1 539 samples of anal swabs and throat swabs fron key practitioners were collected, all samples were confirmed to be negative for SARS-CoV-2 nucleic acid testing. A total of 299 blood samples were collected from key employees, and virus specific serum antibodies IgM and IgG were negative. Conclusion The imported frozen food and key environment sold in the Nantong have not been contaminated with SARS-CoV-2 temporarily, and it remains being necessary to further implements surveillance and control of Corona Virus Disease 2019(COVID-19). © 2022, Editorial Department of Medical Pest Control. All rights reserved.

CLEARED Global consortium results highlight regional variation and need for equity in inpatient outcomes in hospitalized patients with Chronic Liver Disease

Background and aims: A global study with equitable participation for cirrhosis and chronic liver disease (CLD) outcomes is needed. We initiated the Chronic Liver disease Evolution And Registry for Events and Decompensation (CLEARED) study to provide this global perspective. Aim to evaluate determinants of inpatient mortality and organ dysfunction in a multi-center worldwide study. Method: We prospectively enrolled pts with CLD/Cirrhosis >18 years without organ transplant or COVID-19 who were admitted non-electively. To maintain equity in outcome analysis, a maximum of 50 pts/site were allowed. Data for admission variables, hospital course, and inpatient outcomes (ICU, death, organ dysfunction [ODF]) were recorded. This was analyzed for death and ODs using significant variables on admission and including World Bank classification of low/middle-income countries (LMIC). A model for in-hospital mortality for all variables during the hospital course, including ODs) was analyzed. Results: 1383 pts (55 ± 13 yrs, 64% men, 39% White, 30% Asian, 10% Hispanic, 9% Black, 12% other) were enrolled from 49 centers (Fig A). 39% were from high-income while the rest were from LMICs. Admission MELDNa 23 (6–40) with history in past 6 months of hospitalizations 51%, infections 25%, HE 32%, AKI 23%, prior LVP 15%, hydrothorax 8% and HCC 4%. Leading etiologies were Alcohol 46% then NASH 23%, HCV 11% and HBV 13%. Most were on lactulose 52%, diuretics 53%, PPI 49% and statins 11%, SBP prophylaxis 16%, beta-blockers 35% and rifaximin 31%. 90% were admitted for liver-related reasons;GI bleed 30%, HE 34%, AKI 33%, electrolyte issues 30%, anasarca 24% and 25% admission infections. In-hospital course: Median LOS was 7 (1–140) days with 25% needing ICU. 15% died in hospital, 3% were transplanted, 46% developed AKI,15% grade 3–4 HE, 14% shock, 13% nosocomial infections and 13% needed ventilation. Logistic Regression: Fig B shows that liver-related/unrelated factors on admission which predicted in-hospital mortality and development of organ dysfunction with MELDNa and Infections being common among all models. Nosocomial infections and organ dysfunctions predicted mortality when all variables were considered. High-income countries had better mortality outcomes likely due to transplant and ICU availability. AUCs were >0.75 (Figure Presented) Conclusion: In this worldwide equitable experience, admission cirrhosis severity and infections are associated with inpatient outcomes, which are greater in low-income settings. Liver-related and unrelated factors and regional variations are important in defining critical care goals and outcome models in inpatients with cirrhosis.

Drug discovery for coronavirus disease 2019 based on text mining and biomedical databases

Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway. © 2022 Tianjin Press of Chinese Herbal Medicines. All Rights Reserved.

Public Opinion Analysis and Popularity Prediction for COVID-19 Hot Search Based on Weibo

Since the end of 2019, a new type of coronavirus pneumonia (COVID-19) has broken out in Wuhan, and various topics about the development of the epidemic have spread in full swing on the Sina Weibo. In this paper, the web crawler is used to capture the relevant Weibo and popularity of the hot searches during the COVID-19 outbreak, and the Weibo related to the epidemic are extracted by the Bayesian text classification method. Then, the potential Dirichlet model (LDA) was established to obtain the public opinion topic model, and ten public opinion topics were obtained to analyze the public opinion changes with the development of the epidemic. According to the topic model and the influence of daily time point on the popularity of Weibo, a multiple linear regression model is established to predict the popularity. Real-time analysis of changes in public opinion concerns provides reference for decision-making on epidemic prevention and control and information release. © 2022 IEEE.

Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science. Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. Here, we introduce a novel framework that can extract the COVID-19 public health evidence knowledge graph (CPHE-KG) from papers relating to a modelling study. We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process. We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset (CPHIE). We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++ based on the dataset. Leveraging the model on the new corpus, we construct CPHE-KG containing 60,967 entities and 51,140 relations. Finally, we seek to apply our KG to support evidence querying and evidence mapping visualization. Our SS-DYGIE++(SpanBERT) model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks. It has also shown high performance in the relation identification task. With evidence querying, our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions. The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic. Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health. © 2021

A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media

The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the early stage of COVID-19. Our method performs a semantic-based quantitative analysis to unveil essential spatial-temporal characteristics of COVID-19 stigmatization for timely alerts and risk mitigation. Empirical evaluations are carried out to examine our method's predictive utilities. The visualization results of the co-occurrence network using Gephi indicate two distinct groups of stigmatized words that pertain to people in Wuhan and their dietary behaviors, respectively. Netizens' participations and stigmatizations in the Hubei region, where the COVID-19 broke out, are twice (p < 0.05) and four (p <0.01) times more frequent and intense than those in other parts of China, respectively. Also, the number of COVID-19 patients is correlated with COVID-19-related stigma significantly (correlation coefficient = 0.838, p <0.01). The responses to individual users' posts have the power law distribution, while posts by official media appear to attract more responses (e.g., likes, replies, and forward). Our method can help platforms and government agencies manage public health disasters through effective identification and detailed analyses of social stigma on social media.

On potential debt monetisation for China's post-Covid recovery: what can we learn from the past?

A measure of the degree of debt monetisation is constructed for its impact on the business cycle, to be studied in a standard VAR model. Debt monetisation is hardly stimulative, as it raises public demand that crowds out almost as much demand from the private sector. However, it generates inflation, presumably because of inflationary expectations. Nevertheless the impact of debt monetisation on business cycle dynamics is trivial, given the low efficiency of the monetary transmission mechanism. Unless policy proposals are for extraordinarily aggressive moves, or they are accompanied by monetary reforms which facilitate monetary transmission, the recent debate on debt monetisation, we argue, possesses more theoretical meaning than practical meaning for China's postCovid recovery.

Evaluating the Impact of Vaccination on COVID-19 Pandemic Used a Hierarchical Weighted Contact Network Model

The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs. © 2021 IEEE.