In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed （SIR） model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on ...In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed （SIR） model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the finaJ size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.展开更多
AIM To investigate the role of interferon regulatory factor 5(IRF5) in reversing polarization of lung macrophages during severe acute pancreatitis(SAP) in vitro.METHODS A mouse SAP model was established by intraperito...AIM To investigate the role of interferon regulatory factor 5(IRF5) in reversing polarization of lung macrophages during severe acute pancreatitis(SAP) in vitro.METHODS A mouse SAP model was established by intraperitoneal(ip) injections of 20 μg/kg body weight caerulein. Pathological changes in the lung were observed by hematoxylin and eosin staining. Lung macrophages were isolated from bronchoalveolar lavage fluid. The quantity and purity of lung macrophages were detectedby fluorescence-activated cell sorting and evaluated by real-time polymerase chain reaction(RT-PCR). They were treated with IL-4/IRF5 specific siR NA(IRF5 siR NA) to reverse their polarization and were evaluated by detecting markers expression of M1/M2 using RTPCR.RESULTS SAP associated acute lung injury(ALI) was induced successfully by ip injections of caerulein, which was confirmed by histopathology. Lung macrophages expressed high levels of IRF5 as M1 phenotype during the early acute pancreatitis stages. Reduction of IRF5 expression by IRF5 siR NA reversed the action of macrophages from M1 to M2 phenotype in vitro. The expressions of M1 markers, including IRF5(S + IRF5 siR NA vs S + PBS, 0.013 ± 0.01 vs 0.054 ± 0.047, P < 0.01), TNF-α(S + IRF5 siR NA vs S + PBS, 0.0003 ± 0.0002 vs 0.019 ± 0.018, P < 0.001), iN OS(S + IRF5 siR NA vs S + PBS, 0.0003 ± 0.0002 vs 0.026 ± 0.018, P < 0.001) and IL-12(S + IRF5 si RNA vs S + PBS, 0.000005 ± 0.00004 vs 0.024 ± 0.016, P < 0.001), were decreased. In contrast, the expressions of M2 markers, including IL-10(S + IRF5 siR NA vs S + PBS, 0.060 ± 0.055 vs 0.0230 ± 0.018, P < 0.01) and Arg-1(S + IRF5 siR NA vs S + PBS, 0.910 ± 0.788 vs 0.0036 ± 0.0025, P < 0.001), were increased. IRF5 si RNA could reverse the lung macrophage polarization more effectively than IL-4.CONCLUSION Treatment with IRF5 siR NA can reverse the pancreatitisinduced activation of lung macrophages from M1 phenotype to M2 phenotype in SAP associated with ALI.展开更多
Since the COVID-19 outbreak in Wuhan City in December of 2019,numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported.These model predictions have shown a wide range o...Since the COVID-19 outbreak in Wuhan City in December of 2019,numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported.These model predictions have shown a wide range of variations.In our study,we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations.Using the Akaike Information Criterion(AIC)for model selection,we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data.This indicates that predictions using more complex models may not be more reliable compared to using a simpler model.We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23,2020.We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic,and modeling the potential of a second outbreak after the return-to-work in the city.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos.61103231,61103230the Innovation Program of Graduate Scientific Research in Institution of Higher Education of Jiangsu Province of China under Grant No.CXZZ110401+1 种基金the Basic Research Foundation of Engineering University of the Chinese People's Armed Police Force under Grant No.WJY201218 the Natural Science Basic Research Plan in Shaanxi Province of China under Grant No.2011JM8012
文摘In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed （SIR） model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the finaJ size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.
基金Supported by Graduate Innovative Projects in Jiangsu Province,No.1201270052Zhenjiang Science and Technology Program,No.SH2013032+2 种基金National Natural Science Foundation of China,No.81672348Six-Major-Peak-Talent Project of Jiangsu Province of China,No.2015-WSW-014the Scientific Research Fund for the Returned Overseas Chinese Scholars,State Ministry of Education,No.the 50th batch,2015
文摘AIM To investigate the role of interferon regulatory factor 5(IRF5) in reversing polarization of lung macrophages during severe acute pancreatitis(SAP) in vitro.METHODS A mouse SAP model was established by intraperitoneal(ip) injections of 20 μg/kg body weight caerulein. Pathological changes in the lung were observed by hematoxylin and eosin staining. Lung macrophages were isolated from bronchoalveolar lavage fluid. The quantity and purity of lung macrophages were detectedby fluorescence-activated cell sorting and evaluated by real-time polymerase chain reaction(RT-PCR). They were treated with IL-4/IRF5 specific siR NA(IRF5 siR NA) to reverse their polarization and were evaluated by detecting markers expression of M1/M2 using RTPCR.RESULTS SAP associated acute lung injury(ALI) was induced successfully by ip injections of caerulein, which was confirmed by histopathology. Lung macrophages expressed high levels of IRF5 as M1 phenotype during the early acute pancreatitis stages. Reduction of IRF5 expression by IRF5 siR NA reversed the action of macrophages from M1 to M2 phenotype in vitro. The expressions of M1 markers, including IRF5(S + IRF5 siR NA vs S + PBS, 0.013 ± 0.01 vs 0.054 ± 0.047, P < 0.01), TNF-α(S + IRF5 siR NA vs S + PBS, 0.0003 ± 0.0002 vs 0.019 ± 0.018, P < 0.001), iN OS(S + IRF5 siR NA vs S + PBS, 0.0003 ± 0.0002 vs 0.026 ± 0.018, P < 0.001) and IL-12(S + IRF5 si RNA vs S + PBS, 0.000005 ± 0.00004 vs 0.024 ± 0.016, P < 0.001), were decreased. In contrast, the expressions of M2 markers, including IL-10(S + IRF5 siR NA vs S + PBS, 0.060 ± 0.055 vs 0.0230 ± 0.018, P < 0.01) and Arg-1(S + IRF5 siR NA vs S + PBS, 0.910 ± 0.788 vs 0.0036 ± 0.0025, P < 0.001), were increased. IRF5 si RNA could reverse the lung macrophage polarization more effectively than IL-4.CONCLUSION Treatment with IRF5 siR NA can reverse the pancreatitisinduced activation of lung macrophages from M1 phenotype to M2 phenotype in SAP associated with ALI.
基金Research of MYL is supported in part by the Natural Science and Engineering Research Council(NSERC)of Canada and Canada Foundation for Innovation(CFI).
文摘Since the COVID-19 outbreak in Wuhan City in December of 2019,numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported.These model predictions have shown a wide range of variations.In our study,we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations.Using the Akaike Information Criterion(AIC)for model selection,we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data.This indicates that predictions using more complex models may not be more reliable compared to using a simpler model.We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23,2020.We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic,and modeling the potential of a second outbreak after the return-to-work in the city.
