BACKGROUND Sepsis often causes severe liver injury and leads to poor patient outcomes.Early detection of sepsis-induced liver injury(SILI)and early treatment are key to improving outcomes.AIM To investigate the clinic...BACKGROUND Sepsis often causes severe liver injury and leads to poor patient outcomes.Early detection of sepsis-induced liver injury(SILI)and early treatment are key to improving outcomes.AIM To investigate the clinical characteristics of SILI patients and analyze the associated risk factors,to identify potential sensitive biomarkers.METHODS Retrospective analysis of clinical data from 546 patients with sepsis treated in the intensive care unit of the 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force between May 2018 and December 2022.The patients were divided into the sepsis group(n=373)and SILI group(n=173)based on the presence of acute liver injury within 2 hours of admission.We used the random forest algorithm to analyze risk factors and assessed potential diagnostic markers of SILI using the area under the receiver operating characteristic curve,Kaplan-Meier survival curves,subgroup analysis and correlation analysis.RESULTS Compared with the sepsis group,tissue plasminogen activator-inhibitor complex(t-PAIC)levels in serum were significantly higher in the SILI group(P<0.05).Random forest results showed that t-PAIC was an independent risk factor for SILI,with an area under the receiver operating characteristic curve of 0.862(95%confidence interval:0.832-0.892).Based on the optimal cut-off value of 11.9 ng/mL,patients at or above this threshold had significantly higher levels of lactate and Acute Physiology and Chronic Health Evaluation II score.The survival rate of these patients was also significantly worse(hazard ratio=2.2,95%confidence interval:1.584-3.119,P<0.001).Spearman’s correlation coefficients were 0.42 between t-PAIC and lactate,and 0.41 between t-PAIC and aspartate transaminase.Subgroup analysis showed significant differences in t-PAIC levels between patients with different severity of liver dysfunction.CONCLUSION T-PAIC can serve as a diagnostic indicator for SILI,with its elevation correlated with the severity of SILI.展开更多
We study the global (in time) existence of nonnegative solutions of the Gierer-Meinhardt system with mixed boundary conditions. In the research, the Robin boundary and Neumann boundary conditions were used on the acti...We study the global (in time) existence of nonnegative solutions of the Gierer-Meinhardt system with mixed boundary conditions. In the research, the Robin boundary and Neumann boundary conditions were used on the activator and the inhibitor conditions respectively. Based on the priori estimates of solutions, the considerable results were obtained.展开更多
with Neumann boundary conditions. We show that the solutions of the model are bounded all the time for each pair of initial values if r>p-1 and rq>(p-1)(s-1), and that they will blow up in a finite time for some...with Neumann boundary conditions. We show that the solutions of the model are bounded all the time for each pair of initial values if r>p-1 and rq>(p-1)(s-1), and that they will blow up in a finite time for some initial values if either r>p-1 with rq<(p-1)(s+1) or r<p-1.展开更多
Purpose:Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy.Although traditional statistical methods or machine learning(ML)algorithms have been proposed to predict...Purpose:Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy.Although traditional statistical methods or machine learning(ML)algorithms have been proposed to predict septic shock,these potential approaches have never been systematically compared.The present work aimed to develop and compare models to predict septic shock among patients with sepsis.Methods:It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022.Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training(n=311)and validation(n=173)sets.All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected.We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort.The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots.Decision curve analysis was used to evaluate the net benefit of the models.The validation set was applied to verify the predictive accuracy of the models.This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm.Results:Among all patients,37.2% experienced septic shock.The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets,respectively.The model with the best prediction performance was based on the support vector machine(SVM)algorithm,which was constructed by age,tissue plasminogen activator-inhibitor complex,prothrombin time,international normalized ratio,white blood cells,and platelet counts.The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis.Conclusion:The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock.Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.展开更多
This paper deals with the existence and nonexistence of global positive solutions of initial and boundary value problem for the general activator-inhibitor model In this paper, we do not restrict ourselves to the init...This paper deals with the existence and nonexistence of global positive solutions of initial and boundary value problem for the general activator-inhibitor model In this paper, we do not restrict ourselves to the initial data 1/uo, 1/uo ∈L∞(Ω). We prove that there exist glolal solutions if 0 ≤ u0 ≤ u0 and they will blow up in finite time if 0 ≤ v0 ＜ u0 whether u0, v0 are small or large.展开更多
基金Supported by National Key R&D Program of China,No.2022YFC2304600.
文摘BACKGROUND Sepsis often causes severe liver injury and leads to poor patient outcomes.Early detection of sepsis-induced liver injury(SILI)and early treatment are key to improving outcomes.AIM To investigate the clinical characteristics of SILI patients and analyze the associated risk factors,to identify potential sensitive biomarkers.METHODS Retrospective analysis of clinical data from 546 patients with sepsis treated in the intensive care unit of the 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force between May 2018 and December 2022.The patients were divided into the sepsis group(n=373)and SILI group(n=173)based on the presence of acute liver injury within 2 hours of admission.We used the random forest algorithm to analyze risk factors and assessed potential diagnostic markers of SILI using the area under the receiver operating characteristic curve,Kaplan-Meier survival curves,subgroup analysis and correlation analysis.RESULTS Compared with the sepsis group,tissue plasminogen activator-inhibitor complex(t-PAIC)levels in serum were significantly higher in the SILI group(P<0.05).Random forest results showed that t-PAIC was an independent risk factor for SILI,with an area under the receiver operating characteristic curve of 0.862(95%confidence interval:0.832-0.892).Based on the optimal cut-off value of 11.9 ng/mL,patients at or above this threshold had significantly higher levels of lactate and Acute Physiology and Chronic Health Evaluation II score.The survival rate of these patients was also significantly worse(hazard ratio=2.2,95%confidence interval:1.584-3.119,P<0.001).Spearman’s correlation coefficients were 0.42 between t-PAIC and lactate,and 0.41 between t-PAIC and aspartate transaminase.Subgroup analysis showed significant differences in t-PAIC levels between patients with different severity of liver dysfunction.CONCLUSION T-PAIC can serve as a diagnostic indicator for SILI,with its elevation correlated with the severity of SILI.
文摘We study the global (in time) existence of nonnegative solutions of the Gierer-Meinhardt system with mixed boundary conditions. In the research, the Robin boundary and Neumann boundary conditions were used on the activator and the inhibitor conditions respectively. Based on the priori estimates of solutions, the considerable results were obtained.
文摘with Neumann boundary conditions. We show that the solutions of the model are bounded all the time for each pair of initial values if r>p-1 and rq>(p-1)(s-1), and that they will blow up in a finite time for some initial values if either r>p-1 with rq<(p-1)(s+1) or r<p-1.
文摘Purpose:Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy.Although traditional statistical methods or machine learning(ML)algorithms have been proposed to predict septic shock,these potential approaches have never been systematically compared.The present work aimed to develop and compare models to predict septic shock among patients with sepsis.Methods:It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022.Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training(n=311)and validation(n=173)sets.All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected.We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort.The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots.Decision curve analysis was used to evaluate the net benefit of the models.The validation set was applied to verify the predictive accuracy of the models.This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm.Results:Among all patients,37.2% experienced septic shock.The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets,respectively.The model with the best prediction performance was based on the support vector machine(SVM)algorithm,which was constructed by age,tissue plasminogen activator-inhibitor complex,prothrombin time,international normalized ratio,white blood cells,and platelet counts.The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis.Conclusion:The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock.Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
文摘This paper deals with the existence and nonexistence of global positive solutions of initial and boundary value problem for the general activator-inhibitor model In this paper, we do not restrict ourselves to the initial data 1/uo, 1/uo ∈L∞(Ω). We prove that there exist glolal solutions if 0 ≤ u0 ≤ u0 and they will blow up in finite time if 0 ≤ v0 ＜ u0 whether u0, v0 are small or large.
