Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Glymphatic flow has been proposed to clear brain waste while we sleep.Cerebrospinal fluid moves from periarterial to perivenous spaces through the parenchyma,with subsequent cerebrospinal fluid drainage to dural lymph...Glymphatic flow has been proposed to clear brain waste while we sleep.Cerebrospinal fluid moves from periarterial to perivenous spaces through the parenchyma,with subsequent cerebrospinal fluid drainage to dural lymphatics.Glymphatic disruption is associated with neurological conditions such as Alzheimer’s disease and traumatic brain injury.Therefore,investigating its structure and function may improve understanding of pathophysiology.The recent controversy on whether glymphatic flow increases or decreases during sleep demonstrates that the glymphatic hypothesis remains contentious.However,discrepancies between different studies could be due to limitations of the specific techniques used and confounding factors.Here,we review the methods used to study glymphatic function and provide a toolkit from which researchers can choose.We conclude that tracer analysis has been useful,ex vivo techniques are unreliable,and in vivo imaging is still limited.Finally,we explore the potential for future methods and highlight the need for in vitro models,such as microfluidic devices,which may address technique limitations and enable progression of the field.展开更多
Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and...Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and resources,adding a substantial burden to the healthcare system and patients'families.In this context,chondroitinase ABC,a bacterial enzyme isolated from Proteus vulgaris that is modified to facilitate expression and secretion in mammals,has emerged as a promising therapeutic agent.It works by degrading chondroitin sulfate proteoglycans,cleaving the glycosaminoglycanchains of chondroitin sulfate proteoglycans into soluble disaccharides or tetrasaccharides.Chondroitin sulfate proteoglycans are potent axon growth inhibitors and principal constituents of the extracellular matrix surrounding glial and neuronal cells attached to glycosaminoglycan chains.Chondroitinase ABC has been shown to play an effective role in promoting recovery from acute and chronic spinal cord injury by improving axonal regeneration and sprouting,enhancing the plasticity of perineuronal nets,inhibiting neuronal apoptosis,and modulating immune responses in various animal models.In this review,we introduce the classification and pathological mechanisms of spinal cord injury and discuss the pathophysiological role of chondroitin sulfate proteoglycans in spinal cord injury.We also highlight research advancements in spinal cord injury treatment strategies,with a focus on chondroitinase ABC,and illustrate how improvements in chondroitinase ABC stability,enzymatic activity,and delivery methods have enhanced injured spinal cord repair.Furthermore,we emphasize that combination treatment with chondroitinase ABC further enhances therapeutic efficacy.This review aimed to provide a comprehensive understanding of the current trends and future directions of chondroitinase ABC-based spinal cord injury therapies,with an emphasis on how modern technologies are accelerating the optimization of chondroitinase ABC development.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
背景:炎症是脑卒中病理生理过程的关键组成部分,然而脑卒中与炎症之间的因果关系仍不清楚。目的:采用孟德尔随机化及分子对接技术探索91种靶向炎症细胞因子的脑卒中治疗机制。方法:从开放全基因组关联研究数据库(IEU Open GWAS,https://...背景:炎症是脑卒中病理生理过程的关键组成部分,然而脑卒中与炎症之间的因果关系仍不清楚。目的:采用孟德尔随机化及分子对接技术探索91种靶向炎症细胞因子的脑卒中治疗机制。方法:从开放全基因组关联研究数据库(IEU Open GWAS,https://gwas.mrcieu.ac.uk/,由英国布里斯托大学医学研究委员会综合流行病学单位主办)中获得炎症细胞因子及脑卒中的数据,使用逆方差加权法作为主要研究方法进行两样本孟德尔随机化分析,评估91种炎症细胞因子与脑卒中之间的因果关系。随后基于孟德尔随机化研究结果进行了基因本体分析和京都基因与基因组通路分析,并构建了蛋白质-蛋白质相互作用网络。使用美国西奈山伊坎医学院建立的Enrichr数据库(http://amp.pharm.mssm.edu/Enrichr)和美国科罗拉多大学丹佛分校建立的药物特征数据库(http://tanlab.ucdenver.edu/dsigdb)进行脑卒中治疗药物预测,并使用AutoDock软件进行分子对接,通过Discovery Studio2019对结果进行可视化。结果与结论:(1)发现11种炎症细胞因子与全因脑卒中风险之间存在显著的因果关联;9种炎症细胞因子与缺血性脑卒中风险呈强相关;6种细胞因子与大动脉脑卒中风险显著相关;7种炎症细胞因子与心源性栓塞性脑卒中风险呈显著因果关系;12种细胞因子与小血管脑卒中风险显著相关;3种炎症细胞因子与脑内出血风险具有显著的因果关联;(2)基因本体分析和京都基因与基因组通路分析揭示,炎症细胞因子在代谢、炎症及免疫反应等方面对脑卒中具有重要影响;(3)通过蛋白质-蛋白质相互作用网络分析,筛选出与脑卒中密切相关的10种炎症细胞因子;(4)药物预测和分子对接结果表明,阿托伐他汀和氟氢可的松与关键核心靶点白细胞介素18和CCL3的结合力较高;(5)此次研究的数据来源于国际数据库中的欧洲人群,所获得的结果可为中国脑卒中的遗传流行病学研究提供参考;(6)此次研究阐明了炎症细胞因子与脑卒中之间的因果关系,揭示了炎症细胞因子治疗脑卒中的机制,为脑卒中的治疗提供了新思路。展开更多
目的:运用网状Meta分析评估不同免疫吸附柱治疗类风湿关节炎的有效性与安全性,为临床诊治提供循证依据。方法:计算机检索维普、万方、中国知网、PubMed、CBM、CochraneLibrary、Web of Science等数据库,检索公开发表的免疫吸附柱治疗类...目的:运用网状Meta分析评估不同免疫吸附柱治疗类风湿关节炎的有效性与安全性,为临床诊治提供循证依据。方法:计算机检索维普、万方、中国知网、PubMed、CBM、CochraneLibrary、Web of Science等数据库,检索公开发表的免疫吸附柱治疗类风湿关节炎的研究,检索时限至2024年8月。采用Cochrane 5.4手册对纳入的随机对照试验进行质量评价,采用纽卡斯尔-渥太华量表(NOS)对回顾性队列研究进行质量评价。运用R4.1.1软件进行贝叶斯网状Meta分析。结果:最终纳入13篇研究,总样本量891例,共有4种免疫吸附柱。网状Meta分析结果表明,降低C-反应蛋白前3名排序:HA280型吸附柱+常规西药>PH-350型吸附柱+常规西药>A蛋白吸附柱;降低红细胞沉降率前3名排序:白细胞吸附柱>HA280型吸附柱+常规西药>PH-350型吸附柱+常规西药;降低关节肿胀计数前3名排序:白细胞吸附柱>A蛋白吸附柱+常规西药>PH-350型吸附柱+常规西药;降低关节压痛计数前3名排序:白细胞吸附柱>A蛋白吸附柱+常规西药>PH-350型吸附柱+常规西药;降低患者对疾病活动性评分前3名排序:PH-350型吸附柱+常规西药>白细胞吸附柱>A蛋白吸附柱;降低目测类比评分前3名排序:PH-350型吸附柱+常规西药>A蛋白吸附柱>白细胞吸附柱;降低医师对疾病活动性评分前3名排序:PH-350型吸附柱+常规西药>白细胞吸附柱>常规西药。结论:基于纳入的13篇文献证据表明,在降低C-反应蛋白方面,HA280型吸附柱联合常规西药作为首选;在降低红细胞沉降率、关节肿胀计数、关节压痛计数方面,白细胞吸附柱作为首选;在降低患者对疾病活动性评分、医师对疾病活动性评分及目测类比评分方面,PH-350型吸附柱联合常规西药作为首选,在临床中可根据患者的具体情况合理选择不同的免疫吸附柱。展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the European Union Horizon 2020 Research and Innovation Programme(Marie Skłodowska-Curie grant agreement No 847419)supported by the Biotechnology and Biological Sciences Research Council via a Discovery Fellowship(BB/W00934X/1)+6 种基金the Aston University RKE Pump Priming Programmefunded by UKRI’s Research England as part of their Expanding Excellence in England(E3)fundsupported by a UKRI Frontier Research Grant EP/Y023684/1(following assessment as an ERC Advanced grant,FORTIFY,ERC-2022-ADG-101096882 under the UK Government Guarantee scheme)acknowledged a Biotechnology and Biological Sciences Research Council Pioneer Award(BB/Y512874/1)MMS was supported by a Medical Research Council Career Development Award(MR/W027119/1)acknowledged support from the BHF Centre of Research Excellence,University of Oxford(grant code:RE/24/130024)a Biotechnology and Biological Sciences Research Council Pioneer Award(BB/Y512874/1).
文摘Glymphatic flow has been proposed to clear brain waste while we sleep.Cerebrospinal fluid moves from periarterial to perivenous spaces through the parenchyma,with subsequent cerebrospinal fluid drainage to dural lymphatics.Glymphatic disruption is associated with neurological conditions such as Alzheimer’s disease and traumatic brain injury.Therefore,investigating its structure and function may improve understanding of pathophysiology.The recent controversy on whether glymphatic flow increases or decreases during sleep demonstrates that the glymphatic hypothesis remains contentious.However,discrepancies between different studies could be due to limitations of the specific techniques used and confounding factors.Here,we review the methods used to study glymphatic function and provide a toolkit from which researchers can choose.We conclude that tracer analysis has been useful,ex vivo techniques are unreliable,and in vivo imaging is still limited.Finally,we explore the potential for future methods and highlight the need for in vitro models,such as microfluidic devices,which may address technique limitations and enable progression of the field.
基金supported by the National Natural Science Foundation of China,No.82002645China Postdoctoral Science Foundation,No.2022M722321Jiangsu Funding Program for Excellent Postdoctoral Talent,No.2022ZB552(all to YH)。
文摘Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and resources,adding a substantial burden to the healthcare system and patients'families.In this context,chondroitinase ABC,a bacterial enzyme isolated from Proteus vulgaris that is modified to facilitate expression and secretion in mammals,has emerged as a promising therapeutic agent.It works by degrading chondroitin sulfate proteoglycans,cleaving the glycosaminoglycanchains of chondroitin sulfate proteoglycans into soluble disaccharides or tetrasaccharides.Chondroitin sulfate proteoglycans are potent axon growth inhibitors and principal constituents of the extracellular matrix surrounding glial and neuronal cells attached to glycosaminoglycan chains.Chondroitinase ABC has been shown to play an effective role in promoting recovery from acute and chronic spinal cord injury by improving axonal regeneration and sprouting,enhancing the plasticity of perineuronal nets,inhibiting neuronal apoptosis,and modulating immune responses in various animal models.In this review,we introduce the classification and pathological mechanisms of spinal cord injury and discuss the pathophysiological role of chondroitin sulfate proteoglycans in spinal cord injury.We also highlight research advancements in spinal cord injury treatment strategies,with a focus on chondroitinase ABC,and illustrate how improvements in chondroitinase ABC stability,enzymatic activity,and delivery methods have enhanced injured spinal cord repair.Furthermore,we emphasize that combination treatment with chondroitinase ABC further enhances therapeutic efficacy.This review aimed to provide a comprehensive understanding of the current trends and future directions of chondroitinase ABC-based spinal cord injury therapies,with an emphasis on how modern technologies are accelerating the optimization of chondroitinase ABC development.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
文摘背景:炎症是脑卒中病理生理过程的关键组成部分,然而脑卒中与炎症之间的因果关系仍不清楚。目的:采用孟德尔随机化及分子对接技术探索91种靶向炎症细胞因子的脑卒中治疗机制。方法:从开放全基因组关联研究数据库(IEU Open GWAS,https://gwas.mrcieu.ac.uk/,由英国布里斯托大学医学研究委员会综合流行病学单位主办)中获得炎症细胞因子及脑卒中的数据,使用逆方差加权法作为主要研究方法进行两样本孟德尔随机化分析,评估91种炎症细胞因子与脑卒中之间的因果关系。随后基于孟德尔随机化研究结果进行了基因本体分析和京都基因与基因组通路分析,并构建了蛋白质-蛋白质相互作用网络。使用美国西奈山伊坎医学院建立的Enrichr数据库(http://amp.pharm.mssm.edu/Enrichr)和美国科罗拉多大学丹佛分校建立的药物特征数据库(http://tanlab.ucdenver.edu/dsigdb)进行脑卒中治疗药物预测,并使用AutoDock软件进行分子对接,通过Discovery Studio2019对结果进行可视化。结果与结论:(1)发现11种炎症细胞因子与全因脑卒中风险之间存在显著的因果关联;9种炎症细胞因子与缺血性脑卒中风险呈强相关;6种细胞因子与大动脉脑卒中风险显著相关;7种炎症细胞因子与心源性栓塞性脑卒中风险呈显著因果关系;12种细胞因子与小血管脑卒中风险显著相关;3种炎症细胞因子与脑内出血风险具有显著的因果关联;(2)基因本体分析和京都基因与基因组通路分析揭示,炎症细胞因子在代谢、炎症及免疫反应等方面对脑卒中具有重要影响;(3)通过蛋白质-蛋白质相互作用网络分析,筛选出与脑卒中密切相关的10种炎症细胞因子;(4)药物预测和分子对接结果表明,阿托伐他汀和氟氢可的松与关键核心靶点白细胞介素18和CCL3的结合力较高;(5)此次研究的数据来源于国际数据库中的欧洲人群,所获得的结果可为中国脑卒中的遗传流行病学研究提供参考;(6)此次研究阐明了炎症细胞因子与脑卒中之间的因果关系,揭示了炎症细胞因子治疗脑卒中的机制,为脑卒中的治疗提供了新思路。
文摘目的:运用网状Meta分析评估不同免疫吸附柱治疗类风湿关节炎的有效性与安全性,为临床诊治提供循证依据。方法:计算机检索维普、万方、中国知网、PubMed、CBM、CochraneLibrary、Web of Science等数据库,检索公开发表的免疫吸附柱治疗类风湿关节炎的研究,检索时限至2024年8月。采用Cochrane 5.4手册对纳入的随机对照试验进行质量评价,采用纽卡斯尔-渥太华量表(NOS)对回顾性队列研究进行质量评价。运用R4.1.1软件进行贝叶斯网状Meta分析。结果:最终纳入13篇研究,总样本量891例,共有4种免疫吸附柱。网状Meta分析结果表明,降低C-反应蛋白前3名排序:HA280型吸附柱+常规西药>PH-350型吸附柱+常规西药>A蛋白吸附柱;降低红细胞沉降率前3名排序:白细胞吸附柱>HA280型吸附柱+常规西药>PH-350型吸附柱+常规西药;降低关节肿胀计数前3名排序:白细胞吸附柱>A蛋白吸附柱+常规西药>PH-350型吸附柱+常规西药;降低关节压痛计数前3名排序:白细胞吸附柱>A蛋白吸附柱+常规西药>PH-350型吸附柱+常规西药;降低患者对疾病活动性评分前3名排序:PH-350型吸附柱+常规西药>白细胞吸附柱>A蛋白吸附柱;降低目测类比评分前3名排序:PH-350型吸附柱+常规西药>A蛋白吸附柱>白细胞吸附柱;降低医师对疾病活动性评分前3名排序:PH-350型吸附柱+常规西药>白细胞吸附柱>常规西药。结论:基于纳入的13篇文献证据表明,在降低C-反应蛋白方面,HA280型吸附柱联合常规西药作为首选;在降低红细胞沉降率、关节肿胀计数、关节压痛计数方面,白细胞吸附柱作为首选;在降低患者对疾病活动性评分、医师对疾病活动性评分及目测类比评分方面,PH-350型吸附柱联合常规西药作为首选,在临床中可根据患者的具体情况合理选择不同的免疫吸附柱。
