BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective pr...BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.展开更多
In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World J...In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.展开更多
In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(I...In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.展开更多
Intensive care unit-acquired weakness(ICU-AW)significantly hampers patient recovery and increases morbidity.With the absence of established preventive strategies,this study utilizes advanced machine learning methodolo...Intensive care unit-acquired weakness(ICU-AW)significantly hampers patient recovery and increases morbidity.With the absence of established preventive strategies,this study utilizes advanced machine learning methodologies to unearth key predictors of ICU-AW.Employing a sophisticated multilayer perceptron neural network,the research methodically assesses the predictive power for ICU-AW,pinpointing the length of ICU stay and duration of mechanical ventilation as pivotal risk factors.The findings advocate for minimizing these elements as a preventive approach,offering a novel perspective on combating ICU-AW.This research illuminates critical risk factors and lays the groundwork for future explorations into effective prevention and intervention strategies.展开更多
The problem considered is a mode Ⅲ crack lying parallel to the interface of an exponential-type functional graded material （FGM） strip bonded to a linear-type FGM substrate with infinite thickness. By applying the ...The problem considered is a mode Ⅲ crack lying parallel to the interface of an exponential-type functional graded material （FGM） strip bonded to a linear-type FGM substrate with infinite thickness. By applying the Fourier integral transform, the problem was reduced as a Cauchy singular integral equation with an unknown dislocation density function. The collocation method based on Chebyshev polynomials proposed by Erdogan and Gupta was used to solve the singular integral equation numerically. With the numerical solution, the effects of the geometrical and physical parameters on the stress intensity factor （SIF） were analyzed and the following conclusions were drawn： （a） The region affected by the interface or free surface varies with the material rigidity, and higher material rigidity will lead to bigger affected region. （b） The SIF of the crack in the affected region and parallel to the micro-discontinuous interface is lower than those of the weak discontinuous cases. Reducing the weak-discontinuity of the interface will be beneficial to decrease the SIF of the interface-parallel crack in the region affected by the interface. （c） The effect of the free surface on SIF is more remarkable than that of the interface, and the latter is still more notable than that of the material rigidity. When the effects of the interface and free surface are fixed, increase of the material rigidity will enhance the value of SIF.展开更多
The Symmetric Galerkin Boundary Element Method is advantageous for the linear elastic fracture and crackgrowth analysis of solid structures,because only boundary and crack-surface elements are needed.However,for engin...The Symmetric Galerkin Boundary Element Method is advantageous for the linear elastic fracture and crackgrowth analysis of solid structures,because only boundary and crack-surface elements are needed.However,for engineering structures subjected to body forces such as rotational inertia and gravitational loads,additional domain integral terms in the Galerkin boundary integral equation will necessitate meshing of the interior of the domain.In this study,weakly-singular SGBEM for fracture analysis of three-dimensional structures considering rotational inertia and gravitational forces are developed.By using divergence theorem or alternatively the radial integration method,the domain integral terms caused by body forces are transformed into boundary integrals.And due to the weak singularity of the formulated boundary integral equations,a simple Gauss-Legendre quadrature with a few integral points is sufficient for numerically evaluating the SGBEM equations.Some numerical examples are presented to verify this approach and results are compared with benchmark solutions.展开更多
Most reinforced concrete(RC)frame structures did not achieve the "strong column-weak beam" failure mode in recent big earthquakes, resulting in a large number of casualties and significant property loss. To ...Most reinforced concrete(RC)frame structures did not achieve the "strong column-weak beam" failure mode in recent big earthquakes, resulting in a large number of casualties and significant property loss. To deal with this serious problem, a new column-beam relative factor was proposed to characterize the relative yield situation of column ends and beam ends. By limiting the column-beam relative factor, RC frame structures could achieve the "strong column-weak beam" failure mode under the excitation of strong ground motions. The limit values of column-beam relative factor were calculated, analyzed and verified by using structural simulation models for corner columns in the bottom story of structures, which are destroyed most seriously in earthquakes. The results show that the limit values should be analyzed under bi-directional ground motion and with different axial compression ratios of columns. The peak ground acceleration(PGA)of ground motions has no significant effect on the limit values, while the type of strong ground motions has a significant effect on the limit values.展开更多
This article obtains some theoretical results on the number of clear two-factor interaction components and weak minimum aberration in an sm-pIVdesign, by considering the number of not clear two-factor interaction comp...This article obtains some theoretical results on the number of clear two-factor interaction components and weak minimum aberration in an sm-pIVdesign, by considering the number of not clear two-factor interaction components of the design.展开更多
BACKGROUND： Recent studies have demonstrated that tumor necrosis factor-like weak inducer of apoptosis （TWEAK） participates in brain edema. However, it is unclear whether blood-brain barrier （BBB） disruption is a...BACKGROUND： Recent studies have demonstrated that tumor necrosis factor-like weak inducer of apoptosis （TWEAK） participates in brain edema. However, it is unclear whether blood-brain barrier （BBB） disruption is associated with TWEAK during the process of brain edema OBJECTIVE： To investigate the effects of TWEAK on BBB permeability in brain edema. DESIGN, TIME AND SETTING： An immunohistochemical observation, randomized, controlled animal experiment was performed at the Laboratory of Neurosurgical Anatomy, Xiangya Medical College, Central South University ＆ Central Laboratory, Third Xiangya Hospital, Central South University between January 2006 and December 2007. MATERIALS： A total of 48 adult Wistar rats were randomly divided into three groups： normal control （n = 8）, sham-operated （n = 8）, and ischemia/reperfusion （n = 32）. Rats from the ischemia/reperfusion group were randomly assigned to four subgroups according to different time points, i.e., 2 hours of ischemia followed by 6 hours （n = 8）, 12 hours （n = 8）, 1 day （n = 8）, or 12 days （n = 8） of reperfusion. METHODS： Focal cerebral ischemia/reperfusion injury was induced by middle cerebral artery occlusion （MCAO） using the suture method in rats from the ischemia/reperfusion group. Thread was introduced at a depth of 17-19 mm. Rats in the sham-operated group were subjected to experimental procedures similar to the ischemia/reperfusion group; however, the introducing depth of thread was 10 mm. The normal control group was not given any intervention. MAIN OUTCOME MEASURES： TWEAK expression was examined by immunohistochemistry; brain water content on the ischemic side was calculated as the ratio of dry to wet tissue weight; BBB permeability was measured by Evans blue extravasation. RESULTS： A total of eight rats died prior to and after surgery and an additional eight rats were randomly entered into the study. Thus 48 rats were included in the final analysis. In the ischemia/reperfusion group, TWEAK-positive cells were present in the ischemic penumbra surrounding the lamellar necrotic region in the fight cerebral hemisphere at 6 hours reperfusion and increased thereafter; by 2 days reperfusion they had reached a peak level, which was significantly higher than the sham-operated and normal control groups （P 〈 0.05）. At 6 hours reperfusion, both brain water content and Evans blue extravasation showed the same tendency for change as TWEAK expression. Pearson correlation analysis results revealed that the degree of TWEAK expression was positively correlated with brain water content （r = 0.892, P 〈 0.05）. CONCLUSION： The present results confirmed that TWEAK was involved in BBB disruption and participated in brain edema following cerebral ischemia.展开更多
基金Supported by Science and Technology Support Program of Qiandongnan Prefecture,No.Qiandongnan Sci-Tech Support[2021]12Guizhou Province High-Level Innovative Talent Training Program,No.Qiannan Thousand Talents[2022]201701.
文摘BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.
基金Supported by China Medical University,No.CMU111-MF-102.
文摘In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.
文摘In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.
文摘Intensive care unit-acquired weakness(ICU-AW)significantly hampers patient recovery and increases morbidity.With the absence of established preventive strategies,this study utilizes advanced machine learning methodologies to unearth key predictors of ICU-AW.Employing a sophisticated multilayer perceptron neural network,the research methodically assesses the predictive power for ICU-AW,pinpointing the length of ICU stay and duration of mechanical ventilation as pivotal risk factors.The findings advocate for minimizing these elements as a preventive approach,offering a novel perspective on combating ICU-AW.This research illuminates critical risk factors and lays the groundwork for future explorations into effective prevention and intervention strategies.
基金the BK 21 Program of South Korea and the National Natural Science Foundation of China(No.50574097).
文摘The problem considered is a mode Ⅲ crack lying parallel to the interface of an exponential-type functional graded material （FGM） strip bonded to a linear-type FGM substrate with infinite thickness. By applying the Fourier integral transform, the problem was reduced as a Cauchy singular integral equation with an unknown dislocation density function. The collocation method based on Chebyshev polynomials proposed by Erdogan and Gupta was used to solve the singular integral equation numerically. With the numerical solution, the effects of the geometrical and physical parameters on the stress intensity factor （SIF） were analyzed and the following conclusions were drawn： （a） The region affected by the interface or free surface varies with the material rigidity, and higher material rigidity will lead to bigger affected region. （b） The SIF of the crack in the affected region and parallel to the micro-discontinuous interface is lower than those of the weak discontinuous cases. Reducing the weak-discontinuity of the interface will be beneficial to decrease the SIF of the interface-parallel crack in the region affected by the interface. （c） The effect of the free surface on SIF is more remarkable than that of the interface, and the latter is still more notable than that of the material rigidity. When the effects of the interface and free surface are fixed, increase of the material rigidity will enhance the value of SIF.
基金support of the National Natural Science Foundation of China(12072011).
文摘The Symmetric Galerkin Boundary Element Method is advantageous for the linear elastic fracture and crackgrowth analysis of solid structures,because only boundary and crack-surface elements are needed.However,for engineering structures subjected to body forces such as rotational inertia and gravitational loads,additional domain integral terms in the Galerkin boundary integral equation will necessitate meshing of the interior of the domain.In this study,weakly-singular SGBEM for fracture analysis of three-dimensional structures considering rotational inertia and gravitational forces are developed.By using divergence theorem or alternatively the radial integration method,the domain integral terms caused by body forces are transformed into boundary integrals.And due to the weak singularity of the formulated boundary integral equations,a simple Gauss-Legendre quadrature with a few integral points is sufficient for numerically evaluating the SGBEM equations.Some numerical examples are presented to verify this approach and results are compared with benchmark solutions.
基金Supported by the National Natural Science Foundation of China(No.51525803)the Scientific and Technological Development Plans of Tianjin Construction System(No.2013-35)+1 种基金International Science&Technology Cooperation Program of China(No.2012DFA70810)the Basic Science Research Foundation of IEM,CEA(No.2013B07)
文摘Most reinforced concrete(RC)frame structures did not achieve the "strong column-weak beam" failure mode in recent big earthquakes, resulting in a large number of casualties and significant property loss. To deal with this serious problem, a new column-beam relative factor was proposed to characterize the relative yield situation of column ends and beam ends. By limiting the column-beam relative factor, RC frame structures could achieve the "strong column-weak beam" failure mode under the excitation of strong ground motions. The limit values of column-beam relative factor were calculated, analyzed and verified by using structural simulation models for corner columns in the bottom story of structures, which are destroyed most seriously in earthquakes. The results show that the limit values should be analyzed under bi-directional ground motion and with different axial compression ratios of columns. The peak ground acceleration(PGA)of ground motions has no significant effect on the limit values, while the type of strong ground motions has a significant effect on the limit values.
基金supported by the China Postdoctoral Science Foundation (20060390169)the Philosophy and Social Science Foundation of China (07CTJ002)+1 种基金the National Natural Science Foundation of China (10671099)Program for New Century Excellent Talents in University(NCET-08-0909)
文摘This article obtains some theoretical results on the number of clear two-factor interaction components and weak minimum aberration in an sm-pIVdesign, by considering the number of not clear two-factor interaction components of the design.
文摘BACKGROUND： Recent studies have demonstrated that tumor necrosis factor-like weak inducer of apoptosis （TWEAK） participates in brain edema. However, it is unclear whether blood-brain barrier （BBB） disruption is associated with TWEAK during the process of brain edema OBJECTIVE： To investigate the effects of TWEAK on BBB permeability in brain edema. DESIGN, TIME AND SETTING： An immunohistochemical observation, randomized, controlled animal experiment was performed at the Laboratory of Neurosurgical Anatomy, Xiangya Medical College, Central South University ＆ Central Laboratory, Third Xiangya Hospital, Central South University between January 2006 and December 2007. MATERIALS： A total of 48 adult Wistar rats were randomly divided into three groups： normal control （n = 8）, sham-operated （n = 8）, and ischemia/reperfusion （n = 32）. Rats from the ischemia/reperfusion group were randomly assigned to four subgroups according to different time points, i.e., 2 hours of ischemia followed by 6 hours （n = 8）, 12 hours （n = 8）, 1 day （n = 8）, or 12 days （n = 8） of reperfusion. METHODS： Focal cerebral ischemia/reperfusion injury was induced by middle cerebral artery occlusion （MCAO） using the suture method in rats from the ischemia/reperfusion group. Thread was introduced at a depth of 17-19 mm. Rats in the sham-operated group were subjected to experimental procedures similar to the ischemia/reperfusion group; however, the introducing depth of thread was 10 mm. The normal control group was not given any intervention. MAIN OUTCOME MEASURES： TWEAK expression was examined by immunohistochemistry; brain water content on the ischemic side was calculated as the ratio of dry to wet tissue weight; BBB permeability was measured by Evans blue extravasation. RESULTS： A total of eight rats died prior to and after surgery and an additional eight rats were randomly entered into the study. Thus 48 rats were included in the final analysis. In the ischemia/reperfusion group, TWEAK-positive cells were present in the ischemic penumbra surrounding the lamellar necrotic region in the fight cerebral hemisphere at 6 hours reperfusion and increased thereafter; by 2 days reperfusion they had reached a peak level, which was significantly higher than the sham-operated and normal control groups （P 〈 0.05）. At 6 hours reperfusion, both brain water content and Evans blue extravasation showed the same tendency for change as TWEAK expression. Pearson correlation analysis results revealed that the degree of TWEAK expression was positively correlated with brain water content （r = 0.892, P 〈 0.05）. CONCLUSION： The present results confirmed that TWEAK was involved in BBB disruption and participated in brain edema following cerebral ischemia.
