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Antecedentes Pocos estudios han investigado el impacto del síndrome metabólico (SM) en la enfermedad por coronavirus 2019 (COVID-19). Describimos las características clínicas y el pronóstico de los pacientes con COVID-19 confirmados con SM durante la hospitalización y después del alta. Métodos Se incluyeron retrospectivamente 233 pacientes con COVID-19 de los hospitales de 8 ciudades de Jiangsu, China. Se describieron las características clínicas de los pacientes con COVID-19 y se analizaron los factores de riesgo de enfermedad grave mediante un análisis de regresión logística. Resultados Cuarenta y cinco (19,3%) de los 233 pacientes de COVID-19 tenían EM. La mediana de edad de los pacientes de COVID-19 con EM fue significativamente mayor que la de los pacientes sin EM (53,0 años frente a 46,0 años, P=0,004). No hubo diferencias significativas en cuanto a los síntomas clínicos, las imágenes de TC torácica anormales y los fármacos de tratamiento entre los dos grupos. Hubo más pacientes con EM que tuvieron enfermedades graves (33,3% frente a 6,4%, P

It is widely acknowledged that the initial spreaders play an important role in the spread of information in complex networks. Thus, a variety of centrality-based methods have been proposed for identifying the most influential spreaders. However, most existing studies overlook the fact that, in real social networks, it is more costly and difficult to convince influential individuals to act as initial spreaders, resulting in a high risk to maximal spreading. In this paper, we address this problem on the basis of the assumption that the activation of large-degree nodes carries a higher risk than that of small-degree nodes. We aim to identify the initial spreaders that most effectively maximize the spreading when considering both the activation risk and the outbreak size of the initial spreaders. Analysis of random networks reveals that the degree of the optimal initial spreaders does not correspond to the largest node degree in the network, but is instead determined by the infection probability and difference in activation risk among nodes with different degrees. We propose a risk-aware metric to identify the most effective spreaders in real networks. Numerical simulations show that this risk-aware metric outperforms the existing benchmark centralities in terms of maximizing the spreading.

The macro social influence is recognized as a non-negligible ingredient in innovation propagation: more adopters in the network lead to a higher adoption tendency for the rest individuals. A recent study to incorporate such a crucial mechanism shows that sufficient intensity of macro-level social influence can cause a change from a continuous to discontinuous transition, further indicating the existence of a tricritical point. Although network localization strength determines the tricritical point, it remains unclear what network quantities govern the performance of localization in regulating innovation diffusion. To address this issue, we herein consider the model incorporating both the micro- and macro-levels social influence. We present a dynamic message-passing method to analytically treat both the outbreak threshold and recovered population, and validate the predictions through agent-based simulations. Extensive analysis on the classical synthetic networks shows that sparsely available connections, and relatively heterogeneous degree distribution, either assortative or extremely disassortative configurations are favorable for continuous transition. In such cases, the employed network can yield a strong localization effect so that the innovation is trapped in the configurations composed of the hubs with high non-backtracking centrality. We further explore the dependence of both tricritical point and localization strength on three structural quantities: network density, heterogeneity, and assortativity, which gives a clear physical picture of the joint effects of the three structure quantities on the localization strength. Finally, we conclude that the core-periphery structure, being sensitive to the change of the three structure quantities, essentially determines localization strength, and further regulates the phase transition., Comment: 23 pages, 10 figures, 1 tables

Previous studies reported that coronavirus disease 2019 (COVID‐19) was likely to result in liver injury. However, few studies reported the impacts of COVID‐19 on liver function in patients with chronic liver diseases. We aimed to describe a case series of COVID‐19 patients with chronic hepatitis B virus (HBV) infection. Confirmed hospitalized COVID‐19 patients from hospitals in 10 cities of Jiangsu province, China were retrospectively included between January 18, 2020, and February 26, 2020. Demographic information, epidemiologic data, clinical features and treatment data were extracted from medical records. Seven COVID‐19 patients with chronic HBV infection were included. Six (85.7%) patients were male. The patients aged from 33 years to 49 years. Two patients had HBV‐related cirrhosis. One patients (14.3%) were positive for serum hepatitis B virus e antigen. On admission, 1 (14.3%) patients had mildly elevated ALT level (>40 U/L) and 1 (14.3%) had elevated AST level (>40 U/L). The serum albumin level and platelet counts were decreased in 2 patients with HBV‐related liver cirrhosis. Three (42.9%) patients had elevated ALT level and 2 (28.6%) patients had elevated AST level in hospitalization. However, the peak ALT and AST level during hospitalization was 51 U/L and 44 U/L, respectively. As of February 29, 2020, all patients were discharged. No patient was admitted to the intensive care units or developed liver failure during hospitalization. The abnormalities of liver function are not uncommon on COVID‐19 patients with chronic HBV infection in our case series. However, no patient developed severe liver‐related complications during hospitalization. This article is protected by copyright. All rights reserved.

The follow-up data of discharged patients with coronavirus disease 19 (COVID-19) have not yet been fully analyzed and reported. This study aimed to evaluate the clinical features, test results, and outcomes of COVID-19 patients after discharge.149 COVID-19 patients with follow-up data after discharge were included. Post-hospitalization data related to clinical features and outcomes were obtained by following the patients up to 6 weeks.The COVID-19 patients were followed for a median of 28.0 days (range of 22 days to 42 days) after discharge from hospital. At the end of follow-up, four patients (2.7%) still had cough. The proportions of leukopenia and lymphopenia were 7.4% and 4.7%, respectively. The proportions of ALT, AST, and Cr abnormalities were 26.2%, 6.0%, and 0%, respectively. Abnormal chest CT was detected in 94 (63.1%) patients, including 14 (9.4%) unilateral pneumonia and 80 (53.7%) bilateral pneumonia. However, the proportion of chest CT abnormality significantly decreased compared to that at the time of admission.One month after discharge, few patients with COVID-19 had clinical symptoms; however, a substantial proportion of COVID-19 patients harbored abnormal laboratory and radiological examinations. Moderately long-term medical follow-up would justifiably benefit COVID-19 patients after discharge.

In signed networks, negative edges represent negative relationships; the increase or gathering of negative interpersonal relationships can lead to social turmoil, which will affect the spreading of diseases or information. Therefore, it is significant to study the impact of the proportion and configuration of negative edges on spreading. In this paper, to study the impact of negative relationships on spreading, we propose a heterogeneous spreading model using signed networks. In this model, we use the balance of the local structure to quantify the probability of contact between individuals, making the contacts heterogeneous. We then examine the impact of negative edges on spreading using numerical simulations. We find that the balance of the network and the spreading coverage (i.e. outbreak size) gradually decrease with the proportion of negative edges. Compared with preference configurations (in which negative signs are placed on edges that have an important impact on spreading), a random configuration (in which negative signs are placed on random edges) has a suppressive effect on spreading. This provides information for epidemic prevention. Finally, we find that there are two important factors — contact probability and spreading paths — that could explain the observed spreading phenomena.

Many real-world systems display opposite relationships and can be depicted as signed networks to study. On signed networks, positive/negative edges mean users like/dislike objects. This information is valuable and should be considered into recommendations. In this paper, we mainly study recommendations on signed networks that contain users’ purchase behaviors as well as attitude information, which not only can validate the accuracy of recommendation algorithms but also measure the users’ satisfaction degree after purchasing. Accordingly, we proposed a new recommender algorithm by defining an index P . We further compared our method to other four classical algorithms on three disparate datasets. The results show the accuracy of our method improves at most three times higher than other classic algorithms on recommending negative edges. In addition, the recommendation diversity of our method performs better than heat conduction algorithm which is generally recognized as an effective algorithm in terms of diversity. For instance, the value of Novelty dropped from 19.74 to 3.04 when comparing the heat conduction algorithm with our method on the Movielens dataset. In a word, our method can recommend the objects that are novel to users and ensure users’ satisfaction after purchasing.

An effective and stable operation of an economic system leads to a prosperous society and sustainable world development. Unfortunately, the system faces inevitable perturbations of extreme events and is frequently damaged. To maintain the system's stability, recovering its damaged functionality is essential and is complementary to strengthening its resilience and forecasting extreme events. This paper proposes a target recovery method based on network and economic equilibrium theories to defend the economic system against perturbations characterized as localized attacks. This novel method stimulates a set of economic sectors that mutually reinforce damaged economic sectors and is intuitively named the target reinforcement path (TRP) method. Developing a nonlinear dynamic model that simulates the economic system's operation after being perturbed by a localized attack and recovering based on a target recovery method, we compute the relaxation time for this process to quantify the method's efficiency. Furthermore, we adopt a rank aggregation method to comprehensively measure the method's efficiency by studying the target recovery of three country-level economic systems (China, India, and Japan) for 73 different regional attack scenarios. Through a comparative analysis of the TRP method and three other classic methods, the TRP method is shown to be more effective and less costly. Applicatively, the proposed method exhibits the potential to recover other vital complex systems with spontaneous recovery ability, such as immune, neurological, and ecological systems.

Background There is still little knowledge about the association of liver fibrosis with the clinical outcomes of COVID‐19 patients with non‐alcoholic fatty liver disease (NAFLD). The aim of the study was to determine the association of NAFLD fibrosis score (NFS)–determined liver fibrosis with clinical outcomes of COVID‐19 patients with NAFLD. Methods The NAFLD was diagnosed by the Hepatic Steatosis Index (HSI) in the absence of other causes of chronic liver diseases. NFS was used to evaluate the severity of liver fibrosis. Results A total of 86 COVID‐19 patients with NAFLD were included. The median age was 43.5 years, and 58.1% of patients were male. Thirty‐eight (44.2%) patients had advanced liver fibrosis according to the NFS. Multivariate analysis indicated that concurrent diabetes (odds ratio [OR] 8.264, 95% confidence interval [CI] 1.202–56.830, p = 0.032) and advanced liver fibrosis (OR 11.057, 95% CI 1.193–102.439, p = 0.034) were independent risk factors of severe illness in COVID‐19 patients with NAFLD. Conclusion NAFLD patients with NFS‐determined advanced liver fibrosis are at higher risk of severe COVID‐19., Concurrent with NAFLD is common in COVID‐19 patients. The presence of advanced liver fibrosis was an independent risk factor of severe illness in COVID‐19 patients with NAFLD.

We aimed to describe liver injury and identify the risk factors of liver injury in coronavirus disease (COVID-19) patients without chronic liver diseases (CLD). The clinical data of 228 confirmed COVID-19 patients without CLD were retrospectively collected from ten hospitals in Jiangsu, China. Sixty-seven (29.4%) of 228 patients without CLD showed abnormal liver function on admission, including increased alanine aminotransferase (ALT) (25 [11.0%]) U/L, aspartate aminotransferase (AST) 30 [13.2%]) U/L, gamma-glutamyl transferase (GGT) 28 [12.4%]) U/L, total bilirubin (Tbil) 16 [7.0%] µmol/L, and alkaline phosphatase (ALP) 10 [4.5%]) U/L. During hospitalization, 129 (56.3%) of 228 patients showed abnormal liver function, including elevated ALT (84 [36.8%]), AST (58 [25.4%]), GGT (67 [29.5%]), and Tbil (59 [25.9%]). Age over 50 years (odds ratio [OR], 2.086; 95% confidence interval [CI], 1.030-4.225; p = .041), male sex (OR, 2.737; 95% CI, 1.418-5.284; p = .003), and lopinavir-ritonavir (OR, 2.504; 95% CI, 1.187-5.283; p = .016) were associated with higher risk of liver function abnormality, while the atomized inhalation of interferon α-2b (OR, 0.256; 95% CI 0.126-0.520; p < .001) was associated with reduced risk of liver function abnormality during hospitalization. Mild to moderate liver injury was common in COVID-19 patients in Jiangsu, China. Age over 50 years, male sex, and lopinavir-ritonavir were the independent risk factors of liver impairment in COVID-19 patients during hospitalization.

BACKGROUND: Reduning injection is a traditional Chinese medicine (TCM) with known efficacy against a variety of viral infections, but there is no data about its efficacy against coronavirus disease 2019 (COVID-19). METHODS: To explore the efficacy and safety of Reduning injection in the treatment of COVID-19, a randomized, open-labeled, multicenter, controlled trial was conducted from 12 general hospitals between 2020.02.06 and 2020.03.23. Patients with COVID-19 who met the diagnostic criteria of the "Diagnosis and Treatment Program for Novel Coronavirus Infection Pneumonia (Trial Fifth Edition)". Patients were randomized to routine treatment with or without Reduning injection (20 mL/day for 14 days) (ChiCTR2000029589). The primary endpoint was the rate of achieving clinical symptom recovery on day 14 of treatment. RESULTS: There were 77 and 80 participants in the Reduning and control groups. The symptom resolution rate at 14 days was higher in the Reduning injection than in controls [full-analysis set (FAS): 84.4% vs. 60.0%, P=0.0004]. Compared with controls, the Reduning group showed shorter median time to resolution of the clinical symptoms (143 vs. 313.5 h, P

Limited data are available for clinical characteristics of patients with coronavirus disease 2019 (COVID-19) outside Wuhan. This study aimed to describe the clinical characteristics of COVID-19 and identify the risk factors for severe illness of COVID-19 in Jiangsu province, China. Clinical data of hospitalized COVID-19 patients were retrospectively collected in 8 hospitals from 8 cities of Jiangsu province, China. Clinical findings of COVID-19 patients were described and risk factors for severe illness of COVID-19 were analyzed. By Feb 10, 2020, 202 hospitalized patients with COVID-19 were enrolled. The median age of patients was 44.0 years (interquartile range, 33.0–54.0). 55 (27.2%) patients had comorbidities. At the onset of illness, the common symptoms were fever (156 [77.2%]) and cough (120 [59.4%]). 66 (32.7%) patients had lymphopenia. 193 (95.5%) patients had abnormal radiological findings. 11 (5.4%) patients were admitted to the intensive care unit and none of the patients died. 23 (11.4%) patients had severe illness. Severe illness of COVID-19 was independently associated with body mass index (BMI) ≥ 28 kg/m2 (odds ratio [OR], 9.219; 95% confidence interval [CI], 2.731 to 31.126; P, Author summary We reported the epidemiology, clinical laboratory, radiological characteristics, treatment, and clinical outcomes of 202 confirmed cases of COVID-19 in 8 hospitals from 8 cities of Jiangsu province, China. In this case series in Jiangsu Province, COVID-19 patients had less severe symptoms and had better outcomes than the initial COVID-19 patients in Wuhan. The BMI ≥ 28 kg/m2 and a known history of type 2 diabetes were independent risk factors of severe illness in patients with COVID-19. Sustained intensive control efforts remain urgently needed to minimize the risk of COVID-19.

Coronavirus disease 2019 (COVID-19) was first detected in China in December 2019, and declared as a pandemic by the World Health Organization (WHO) on March 11, 2020.To study the clinical features of patients with COVID-19, we analyzed the correlation between some inflammation-related indicators in patients' serum and the severity of the disease, especially PV (pneumonia volume under CT scan) and pneumonia volume ratio (PVR).Sixty-six COVID-19 patients in Huai'an, China were selected as the research subjects. We collected the clinical data, including general characteristics, clinical symptoms, serum test results and CT performance, explored the relationship between inflammation-related indexes, oxygenation index, PV, PVR, while indicators of mild to moderate patients and severe patients were compared.The most prominent manifestations of COVID-19 patients were fever (47, 71.2%); cough (41, 62.1%), with or without respiratory and other systemic symptoms; There was no difference in gender (P = .567) and age (P = .865) between mild to moderate and severe groups. High sensitivity C-reactive protein (hs-CRP), erythrocyte sedimentation rate (ESR), and interleukin-6 (IL-6) of overall patients were higher than the normal range (P .001, respectively). hs-CRP was negatively correlated with oxygenation index (OI) (r = -0.55), whereas positively correlated with PV, PVR and ESR (r = 0.89; r = 0.87; r = 0.47, respectively); ESR was negatively correlated with OI (r = -0.45), meanwhile it was positively correlated with PV and PVR (r = 0.44; r = 0.46, respectively). OI was negatively correlated with PV and PVR (r = -0.6, respectively). PV had a clear correlation with PVR (r = 1). Severe patients' hs-CRP, PV, PVR were higher than mild to moderate group (P = .006; P = .001; P .001, respectively), but OI was lower (P .001).The clinical features of COVID-19 were similar to general viral pneumonia. hs-CRP, ESR showed a certain correlation with the PV and PVR, which might play a certain role in assessing the severity of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) threatens the health and safety of all humanity. This disease has a prominent feature: the presymptomatic and asymptomatic viral carriers can spread the disease. It is crucial to estimate the impact of this undetected transmission on epidemic outbreaks. Currently, disease-related information has been widely disseminated by the mass media. To investigate the impact of both individuals and mass media information dissemination on the epidemic spreading, we establish a new UAU-SEIR (Unaware–Aware–Unaware–Susceptible–Exposed–Infected–Recovered) model with mass media on two-layer multiplex networks. In the model, E-state individuals denote asymptomatic infections, and a single node connecting to all individuals denotes the mass media. In this work, we use the Microscopic Markovian Chain Approach (MMCA) to derive the epidemic threshold. Comparing the MMCA theoretical results with Monte Carlo (MC) simulations, we find that the MMCA has a good consistency with MC simulations. In addition, we also analyze the impact of model parameters on epidemic spreading and epidemic threshold. The results show that reducing the proportion of asymptomatic infections, accelerating the dissemination of information between individuals and the dissemination of information via the mass media can effectively inhibit the epidemic spreading and raise the epidemic threshold.

This paper is intended for exploring the multifractal features of tanker freight rate market volatility with the common external factor of crude oil price by both the multifractal cross-correlation analysis method (MF-CCA) and the multifractal detrended partial cross-correlation analysis method (MF-DPXA) with consideration of finite size effect. The multifractal spectrums of original, random and surrogate time series are employed to separate the three components of multifractality, and to uncover the influence of financial crisis and oil price on volatility and cross-correlated fluctuations of the tanker freight rates. After the financial crisis in terms of the generalized Hurst exponent, stronger non-linear characteristic and noticeable anti-persistent characteristic in cross correlations between freight rates are supported by the MF-DPXA analysis. Meanwhile, stronger multifractality is indicated by the MF-CCA analysis. These results deepen the understanding of multifractality in tanker freight rate market, and provide investors, shipping related operators or even market players with insight to adjust their marketing strategies.

The recommendation methods based on network diffusion have been shown to perform well in both recommendation accuracy and diversity. Nowdays, numerous extensions have been made to further improve the performance of such methods. However, to what extent can items be predicted by diffusion-based algorithms still lack of understanding. Here, we mainly propose a method to quantify the predictability of diffusion-based algorithms. Accordingly, we conduct experiments on Movielens and Netflix data sets. The results show that the higher recommendation accuracy based on diffusion algorithms can still be achieved by optimizing the way of resource allocation on a density network. On a sparse network, the possibility of improving accuracy is relatively low due to the fact that the current accuracy of diffusion-based methods is very close its predictability. In this case, we find that the predictability can be improved significantly by multi-steps diffusion, especially for users with less historical information. In contrast to common belief, there are plausible circumstances where the higher predictability of diffusion-based methods do not correspond to those users with more historical recording. Thus, we proposed the diffusion coverage and item average degree to explain this phenomenon. In addition, we demonstrate the recommendation accuracy in real online system is overestimated by random partition used in the literature, suggesting the recommendation in real online system may be a harder task.

It is widely acknowledged that emerging stock markets and mature stock markets differ in many respects, while research on emerging markets is rather insufficient compared to well-studied mature markets. In this paper, we investigate the unique network properties and market pattern of the emerging Chinese stock market of crude oil related companies with the implementation of minimum spanning trees(MST) and hierarchical trees(HT). For a robust quantification of cross-correlation between stocks, detrended cross-correlation coefficient (DCCA coefficient) and partial cross-correlation coefficient (DPXA coefficient) are applied to reveal the overall cross-correlation and identify the common external factor on price fluctuations respectively. The paper depicts the dynamic evolution of Chinese stock market of crude oil related companies, showing the variation of structural density and stability under different time scales and market environments. The establishment of correlation-based networks provides a more pertinent explanation for the unusual market structure and conduct of the emerging Chinese stock market, contributing to both portfolio decisions and risk resistance.