儿童肺炎支原体肺炎肺泡灌洗液CARDS TX、HMGB1、TLR2表达及临床意义

目的研究肺炎支原体肺炎(MPP)患儿肺泡灌洗液中社区获得性呼吸窘迫综合征毒素(CARDS TX)、高迁移率族蛋白1(HMGB1)、Toll样受体2(TLR2)表达,探讨其在MPP发病中的临床意义。方法回顾性分析55例MPP病例组及20例对照组临床资料,同时检测两组支气管肺泡灌洗液(BALF)中CARDS TX、HMGB1、TLR2、TLR4、晚期糖基化终末产物受体(RAGE)及髓样分化因子88(MyD88)表达,体外检测不同浓度CARDS TX刺激下HMGB1、TLR2的表达并进行比较。结果与对照组相比,MPP组LYM绝对值计数、PA、CD3^(+)、CD3^(+)CD4^(+)、CD3^(+)CD8^(+)、CD3-CD19^(+)、NK cell、CD19^(+)CD23^(+)明显下降,CRP、LDH、D-二聚体、IgA、IgG、IgM、CARDS TX、HMGB1、TLR2、MyD88均升高,差异有统计学意义(P<0.05或0.001)。CARDS TX刺激后HMGB1、TLR2的表达升高,且呈正相关,差异有统计学意义(P<0.001)。结论CARDS TX可能通过HMGB1-TLR2-MyD88途径参与肺炎支原体(MP)的致病。

CARD6在脓毒症心肌损伤中的机制研究

目的:探讨CARD6在脓毒症心肌损伤中的调节机制。方法:将C57BL/6小鼠随机分为对照组与模型组。采用LPS腹腔注射法构建脓毒症心肌损伤小鼠模型,RT-qPCR检测小鼠心肌组织中CARD6、caspase-1的mRNA水平。体外实验则采用LPS刺激转染CARD6过表达质粒的H9C2细胞,检测caspase-1、细胞活力、CK-MB水平。结果:与对照组相比,模型组小鼠心肌组织中LDH、CK-MB含量,caspase-1表达水平上升,提示脓毒症导致小鼠心肌损伤,而CARD6表达水平明显降低。体外实验发现CARD6表达水平呈下降趋势,转染含有CARD6的过表达质粒后细胞释放CK-MB的水平降低、细胞活力增加、caspase-1水平降低。结论:CARD6可改善脓毒症所致心肌损伤,其机制可能与caspase-1介导的细胞焦亡相关。

肺炎支原体CARDS毒素依赖KELED序列调控炎性反应

目的细胞内逆向运输系统是微生物毒素蛋白导致细胞毒性的关键。KELED序列是肺炎支原体CARDS毒素逆向转运至内质网,而后发挥细胞毒性作用的关键氨基酸基序。在小鼠模型中,体外重组的CARDS蛋白可以重现肺炎支原体肺炎患者的特征性症状。然而,CARDS是否依赖于KELED序列调控肺组织炎性反应仍不清楚。方法在大肠杆菌中表达并纯化CARDS及其KELED序列突变蛋白;随后在细胞水平验证其致空泡化活性;最后,在小鼠模型中评价KELED序列突变的CARDS蛋白是否影响炎性因子的表达以及是否造成肺组织病理学损伤。结果KELED序列氨基酸发生突变,减弱CARDS蛋白致细胞空泡化效应,下调肺组织炎性因子mRNA表达,减轻肺组织的病理学损伤。结论KELED序列对CARDS蛋白致细胞空泡化及炎性损伤活性的维持至关重要。

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

CARD11 serves as a therapeutic biomarker for the drug therapies of ccRCC

Background:The incidence of clear cell renal cell carcinoma(ccRCC)is globally high;however,despite the introduction of innovative drug therapies,there remains a lack of effective biomarkers for evaluating treatment response.Recently,Caspase recruiting domain-containing protein 11(CARD11)has garnered attention due to its significant association with tumor development and the immune system.Methods:The expression of CARD11 mRNA and protein in ccRCC were analyzed by public database and immunohistochemistry.The focus of this study is on the epigenomic modifications of CARD11,its expression of ccRCC immunophenotype,and its correlation with response to immunotherapy and targeted therapy.Furthermore,to investigate the mechanism of this molecule’s influence on different biological behaviors of cells,cell tests in vitro have been conducted to observe the impact of its expression level.Results:CARD11 expression was upregulated which may be mainly modified by body methylation and was correlated with poor prognosis in ccRCC.In the tumor microenvironment of ccRCC,CARD11 expression was positively correlated with increased T lymphocyte infiltration and increased expression of inhibitory immune checkpoints.Moreover,ccRCC patients with high CARD11 expression had a better response to immunotherapy and targeted therapy.The knockdown of CARD11 ultimately suppressed the proliferation,migration,and invasion capabilities of ccRCC cells while simultaneously enhancing tumor cell apoptosis.Conclusion:We identified CARD11 as a novel therapeutic biomarker for immunotherapy and targeted therapy in ccRCC.

A Study on the Problem of Employee Punch Card Substitution in Enterprise Management:The Case of Jingdong Enterprise

With economic progress and the continuous advancement of science and technology,the issue of employees substituting punch cards has gradually become a significant challenge in enterprise management.The purpose of this paper is to discuss the causes,effects,and countermeasures of the employee punch card phenomenon,with the aim of providing effective management recommendations for Chinese enterprises.In practice,enterprises should flexibly apply the countermeasures proposed in this paper according to their specific circumstances to prevent substitute punch card incidents and improve overall management efficiency.

CARD8基因新发P367L失功能突变患者自身炎症性疾病的临床及免疫学特征

目的报道1例CARD8基因新发突变导致自身炎症性疾病患儿及其家系的临床特征、致病性鉴定与免疫致病机制。方法收集并分析1例CARD8基因突变患儿的突变位点以及临床资料,随访患儿治疗及预后;采用全外显子测序发现可疑致病基因并利用Sanger测序验证家系突变位点;利用体外实验进行致病性验证及免疫学分析。结果先证者为1名男性患儿,主要临床表现为极早发的食物蛋白过敏性肠炎、特应性皮炎及反复发作的上消化道出血。实验室检查提示血清IgE升高。全外显子测序发现CARD8基因新发杂合突变c.1100C>T(p.P367L),且该突变位点经Sanger测序验证证实来源于其父亲。该突变致病性不明确,且既往未见报道。实验室检查提示患儿血浆中多种促炎细胞因子水平显著升高。患儿原代外周血单个核细胞(PBMCs)及转染相同突变质粒的293T细胞CARD8蛋白表达均显著降低。患儿PBMCs中NLRP3炎性小体过度激活,而NF-κB通路无异常。以上研究均提示患儿的CARD8基因p.P367L突变为失功能性致病突变。结论本文报道全球第2例,中国第1例CARD8基因杂合突变患儿及家系,并明确CRAD8基因c.1100C>T的突变致病性。该CARD8基因失功能突变可导致NLRP3炎性小体的过度激活而诱发患儿发生以消化道症状起病的自身炎症性疾病。

携带罕见突变的CARD14相关丘疹鳞屑性疾病一例

患者,男,41岁。因“全身红斑鳞屑40天”来我院就诊,临床、组织病理学具有毛发红糠疹和银屑病的交叉表现,无家族史,首次发病年龄为41岁。初步诊断为I型毛发红糠疹,口服阿维A有显著改善。经过全外显子测序和Sanger测序发现患者携带CARD14基因的罕见致病性突变位点c.1799 C>T(p.Pro600Leu),综合临床、组织病理学表现,以及基因检测的结果,最终诊断为CARD14相关的丘疹鳞屑性疾病。

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.

柴葛芩连汤对解脲脲原体肺炎小鼠肺功能及CARDS TX/NLRP3信号通路的影响

目的探究柴葛芩连汤对解脲脲原体肺炎模型小鼠肺功能以及CARDS TX/NLRP3信号通路的影响。方法60只小鼠分为对照组,肺炎组,柴葛芩连汤低、高剂量组和阿奇霉素组。鼻腔滴注解脲脲原体菌液进行造模,柴葛芩连汤低、高剂量组灌胃5.4、16.2 g/kg柴葛芩连汤;阿奇霉素组灌胃90 mg/kg阿奇霉素溶液2个周期(1周期为7 d,前3 d连续灌胃阿奇霉素溶液,后4 d灌胃等量蒸馏水);对照组和肺炎组灌胃等量蒸馏水,连续14 d。采用动物肺功能分析仪检测肺功能,ELISA法检测肺泡灌洗液中炎症因子水平,HE染色观察肺组织病理变化,RT-qPCR和Western blot法检测肺组织肺炎支原体外毒素(CARDS TX)、炎症小体(NLRP3)、凋亡相关斑点样蛋白(ASC)、半胱氨酸天冬氨酸蛋白酶-1(caspase-1)的mRNA和蛋白表达。结果与对照组比较,肺炎组小鼠PEF、FEV0.1、FEF-50降低(P<0.05),IL-6、TNF-α水平及CARDS TX、NLRP3、ASC、caspase-1 mRNA和蛋白表达均升高(P<0.05),肺泡结构模糊,有炎性细胞浸润;与肺炎组比较,柴葛芩连汤各剂量组和阿奇霉素组小鼠PEF、FEV0.1与FEF-50升高(P<0.05),IL-6、TNF-α水平及CARDS TX、NLRP3、ASC、caspase-1 mRNA和蛋白表达均降低(P<0.05),肺组织病理变化有不同程度缓解。结论柴葛芩连汤可以改善解脲脲原体肺炎小鼠受损的肺功能,该作用可能与抑制CARDS TX/NLRP3信号通路有关。

Effect of a low-cost instruction card for automated external defibrillator operation in lay rescuers: a randomized simulation study

BACKGROUND:To evaluate whether a simplified self-instruction card can help potential rescue providers use automated external defibrillators(AEDs)more accurately and quickly.METHODS:From June 1,2018,to November 30,2019,a prospective longitudinal randomized controlled simulation study was conducted among 165 laypeople(18–65 years old)without prior AED training.A self-instruction card was designed to illuminate key AED operation procedures.Subjects were randomly divided into the card(n=83)and control(n=82)groups with age stratification.They were then individually evaluated in the same simulated scenario to use AED with(card group)or without the self-instruction card(control group)at baseline,posttraining,and at the 3-month follow-up.RESULTS:At baseline,the card group reached a significantly higher proportion of successful defibrillation(31.1%vs.15.9%,P=0.03),fully baring the chest(88.9%vs.63.4%,P<0.001),correct electrode placement(32.5%vs.17.1%,P=0.03),and resuming cardiopulmonary resuscitation(CPR)(72.3%vs.9.8%,P<0.001).At post-training and follow-up,there were no significant differences in key behaviors,except for resuming CPR.Time to shock and time to resume CPR were shorter in the card group,while time to power-on AED was not different in each phase of tests.In the 55–65 years group,the card group achieved more skill improvements over the control group compared to the other age groups.CONCLUSION:The self-instruction card could serve as a direction for first-time AED users and as a reminder for trained subjects.This could be a practical,cost-effective way to improve the AED skills of potential rescue providers among different age groups,including seniors.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.

CARD9在真菌感染性疾病中的作用研究进展

CARD9是衔接先天性免疫和获得性免疫之间的重要纽带,近年来越来越多的疾病显示出与CARD9的缺失或突变的联系,特别是真菌感染性疾病。该文主要从CARD9在真菌免疫方面的作用机制出发,对与CARD9相关的真菌感染性疾病进行概述,探讨CARD9在真菌感染性疾病中的作用。

NOD样受体家族含CARD结构域5在胃癌中的相互作用蛋白及其对胃癌的诊断价值

目的 探究NOD样受体家族含CARD结构域5(NLRC5)在胃癌中的相互作用蛋白,并明确其对胃癌的诊断价值。方法 采用癌症基因组图谱(TCGA)数据库探究NLRC5在胃癌组织和癌旁组织中的差异,结合STRING网站分析NLRC5的相互作用蛋白,通过基因表达综合(GEO)数据库验证TCGA数据库筛选出来的蛋白对胃癌的诊断价值。引用临床模型,验证筛选出来的蛋白在胃癌组织和癌旁组织中的表达情况,确定各蛋白间的相关性,通过受试者工作特征(ROC)曲线明确其对胃癌的诊断价值。结果 TCGA结果显示,胃癌组织中NLRC5表达水平明显高于癌旁组织(P﹤0.01)。蛋白-蛋白相互作用发现,核因子κB激酶复合物抑制剂组分(CHUK)、核因子κB激酶亚单位β的抑制剂(IKBKB)、三重基序蛋白25(TRIM25)与NLRC5均呈正相关。经过GEO数据库验证得到TRIM25、CHUK、IKBKB与NLRC5可以构建联合模型诊断胃癌。临床模型中NLRC5在胃癌组织中高表达,且与TRIM25、IKBKB、CHUK表达水平均呈正相关(P﹤0.01)。NLRC5联合TRIM25、IKBKB、CHUK对胃癌有较高的诊断价值。结论 TRIM25、IKBKB、CHUK参与NLRC5在胃癌发展中的相关调控机制,在胃癌诊断中具有较高的效能。

溃疡性结肠炎与NLRP3/CARD8基因多态性的相关性分析

目的探讨Nod样受体蛋白3(NLRP3)/炎症小体组分基因胱天蛋白酶募集域蛋白8(CARD8)基因多态性对溃疡性结肠炎(ulcerative colitis,UC)患者易感性的影响。方法分别从96例溃疡性结肠炎患者(UC组)和96例非溃疡结肠炎患者(对照组)的外周血液样本中提取DNA,采用聚合酶链式反应(PCR)进行扩增,探针法荧光定量PCR检测NLRP3和CARD8不同点位的基因型,应用ABI7900HT Fast Real-Time PCR System自带软件对读取基因分型。UC组患者均接受硫唑嘌呤(AZA)治疗,分析NLRP3和CARD8基因多态性与治疗后结肠炎性反应的相关性。结果NLRP3基因rs10754558位点包含CC、CG、GG 3种基因型,UC组的CC基因型和C等位基因频率(80.20%、88.02%)低于对照组(95.33%、97.92%),CG、GG及G等位基因型频率(15.63%、4.17%、11.98%)高于对照组(4.17%、0、2.08%);CARD8基因rs2443211位点包含AA、AT、TT 3种基因型,UC组AA基因型和A等位基因频率(54.17%、69.79%)较对照组(93.75%、95.83%)降低,AT、TT及T等位基因频率(31.25%、14.58%、30.21%)较对照组(4.17%、2.08%、4.17%)升高;差异均有统计学意义(P<0.05)。UC组患者接受AZA治疗后,缓解率为77.08%;NLPR3 CC基因型、CARD8 AA基因型患者的肠壁炎性反应缓解率分别高于其他基因型者,差异均有统计学意义(P<0.05)。结论NLRP3 CC基因、CARD8 AA基因在肠壁炎性反应的缓解率中发挥作用,NLRP3/CARD8基因多态性均对溃疡性结肠炎患者易感性有一定的影响。