目的 本研究以脑卒中患者为研究对象,通过二代Illumina高通量测序平台对患者的粪便标本进行微生物群落多样性测序。选择物种丰度≥30%的24个门类（Phylum）作为肠道菌群的研究指标,进而研究肠道菌群与脑卒后抑郁（PSD）之间的相关关系。方法 以40位脑卒中患者的24个门类作为特征变量,抑郁组和对照组为二分类目标变量,建立以Logistic回归、随机森林、支持向量机和AdaBoost为基模型的Stacking分类模型。主成分分析方法作为该模型的特征选择方法选择恰当的主成分进行模型训练,通过二分类评价报告（precision、recall、f1-score）、ROC曲线和混淆矩阵等评价指标对其性能进行评价。结果 （1）通过差异性检验分析了两组（抑郁组和对照组）的基线一致（P<0.05）;（2）从Stacking模型融合的角度定量分析了影响脑卒中后抑郁情绪的具体肠道菌群。研究结果可知,放线菌门、拟杆菌门、变形菌门和酸杆菌门在PSD患者中均增加（P<0.001）;厚壁菌门,疣微菌门,绿弯菌门和软壁菌门在PSD患者中降低（P<0.001）。结论 以上菌群是影响脑卒中后抑郁患者情绪的主要影响因素,因此,在临床上通过恰当干预肠道菌群的变化来调节脑卒中后抑郁患者的抑郁水平,这为脑卒中后抑郁情绪的诊断和治疗方案提供科学依据。

Objective In this study,patients with stroke were selected as the research object,and the microbial community diversity of patients' stool samples was sequenced by the second-generation Illumina high-throughput sequencing platform． Twenty four phylum species with 30% species abundance were selected as indicators for the study of gut microbiota,and then the correlation between gut microbiota and post-stroke depression（PSD） was studied．Methods Taking 24 categories of 40 stroke patients as characteristic variables,depression group and control group as dichotomous target variables,a stacking classification model based on Logistic regression,random forest,support vector machine and AdaBoost was established．As the feature selection method of the model,principal component analysis selects the appropriate principal components for model training,and evaluates its performance through dichotomous evaluation reports（precision,recall,f1 score）,ROC curve and confusion matrix．Results The baseline of the two groups（depression group and control group）was consistent（P<0.05）through the difference test．From the perspective of stacking model fusion,the specific intestinal flora affecting post-stroke depression was quantitatively analyzed．The results showed that Actinobacteria,Bacteroidetes,Proteobacteria and Acidobacteria were significantly increased in PSD patients（P<0.001）,while Firmicutes,Verrucomicrobia,Chloroflexi and Tenericutes were significantly decreased in PSD patients（P<0.001）．Conclusions The above microbiota are the main factors affecting the mood of patients with post-stroke depression．Therefore,in clinical practice,we can adjust the depression level of patients with post-stroke depression by properly intervening the changes of intestinal microbiota,which provides a scientific basis for the diagnosis and treatment of PSD．

目的 观察电针联合重复经颅磁刺激(rTMS)治疗对卒中后抑郁伴失眠患者的疗效并探讨这种联合治疗的机制。方法 对83例PSD患者随机分为rTMS组28例、电针联合rTMS治疗组25例及药物治疗组30例。电针联合rTMS组在对患者进行rTMS治疗基础上予电针治疗2周,并常规给予选择性五羟色胺重摄取抑制剂(SSRI)草酸艾司西酞普兰抗抑郁药物治疗;rTMS组仅采用重复经颅磁刺激治疗2周;药物组给予同种抗抑郁剂治疗。三组于治疗前及治疗2周后接受17项汉密尔顿抑郁量表(HAMD)和匹茨堡睡眠量表(PSQI)评估及多导睡眠监测(PSG)。结果 三组的HAMD评分、PSQI评分及睡眠参数在治疗基线水平均无明显差异。2周后不同治疗组间HAMD计分降低值总体差异有统计学意义(P<0.001)。药物治疗组HAMD计分降低值小于rTMS组和电针联合rTMS组(P<0.05),电针联合rTMS组HAMD计分降低值大于药物组及rTMS组(P<0.05);组间PSQI计分降低值总体差异有统计学意义 (P<0.05)。电针联合rTMS组PSQI计分降低值大于药物组及rTMS组(P<0.05),而药物组及rTMS组之间的PSQI计分降低值无统计学差异(P<0.05);组间PSG中总睡眠时间(F=16.735,P<0.001)及睡眠效率(F=87.548,P<0.001)治疗前后差值总体差异有统计学意义。电针联合rTMS组总睡眠时间的改善优于药物组及rTMS组(P<0.01),而药物组及rTMS组之间总睡眠时间的改善无统计学差异(P<0.05);电针联合rTMS组睡眠效率的提高优于药物组及rTMS组(P<0.001),而药物组及rTMS组之间的睡眠效率的提高无统计学差异(P>0.05)。结论 电针联合rTMS治疗可显著改善PSD的抑郁情绪、睡眠质量及改善总睡眠时间及睡眠效率,效果优于药物治疗组及rTMS组,体现了电针联合rTMS对PSD治疗的增效作用。

Objective To investigate the effect of electric acupuncture combined with Repetitive Transcranial Magnetic Stimulation (rTMS) treatment of poststroke depression with insomnia and analyze the therapeutic mechanism of this method. Methods 83 patients with PSD were randomly divided into the group of electric acupuncture combined with rTMS (n=32), rTMS group (n=32) and drug treatment group (n=32). The patients in the group of electric acupuncture combined with rTMS were given with the electric acupuncture treatment for 2 weeks on the basis of rTMS treatment, and also were regularly and continuously administrated with antidepressant drug (escitalopram citalopram). The rTMS group were only given with rTMS for 2 weeks, and the patients of the drug treatment group were administrated with the same antidepressant. At the baseline and 2th week, the 17-item Hamilton depression scale (17-HAMD), Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) were evaluated. Results The sleep parameters, PSQI scores and HAMD scores among three groups had no significant difference at baseline. After 2 weeks, the overall difference of HAMD score reduction between different treatment groups was statistically significant (P<0.001). The HAMD score reduction in the drug treatment group was less than that in the rTMS group and the electric acupuncture combined rTMS group (P<0.05), and the HAMD score reduction in the electric acupuncture combined rTMS group was greater than that in the drug group and the rTMS group (P<0.05). The overall difference of PSQI score reduction between groups was significant (P<0.05). The PSQI score reduction value of electric acupuncture combined with rTMS group was greater than that of the drug group and the rTMS group (P<0.05), while there was no significant difference in the PSQI score reduction value between the drug group and the rTMS group (P<0.05). The overall difference of total sleep duration (F=16.735,P<0.001) and sleep efficiency(F=87.548,P<0.001) evalted by PSG among groups was significant. The changes of both the total sleep duration and sleep efficiency of electric acupuncture combined with rTMS group was greater than that of the drug group and the rTMS group (P<0.001), while there was no significant difference neither in the changes of total sleep duration nor sleep efficiency between the drug group and the rTMS group before and after treatment among the groups (P>0.05). Conclusion Electroacupuncture combined with rTMS treatment may improve the efficacy of depression, sleep quality, the total sleep duration and sleep efficiency of PSD, and the effect is better than that of the drug treatment group and the rTMS group, which reflects the synergic effect of electroacupuncture combined with rTMS on PSD treatment.

目的 探讨高频经颅磁刺激治疗对 PSD 伴失眠患者的抑郁情绪及睡眠质量的疗效。方法 对63例PSD患者随机分为联合组32例(10Hz高频rTMS+艾司西酞普兰)及药物组31例(艾司西酞普兰+假刺激),每周5次,共治疗4周。于治疗前及治疗后4周末分别对两组患者进行HAMD、PSQI评分及多导睡眠监测。。结果 rTMS 治疗前,2组HAMD、PSQI评分及睡眠参数比较均无差异;治疗后第4周末,两组HAMD评分、PSQI评分、总睡眠时间、睡眠效率及快眼动睡眠期比例均较治疗前改善;研究组HAMD评分下降幅度较对照组明显,而PSQI评分下降幅度及相关睡眠参数改善无差异。结论 高频rTMS治疗对PSD的抑郁症状疗效更明显,而对睡眠质量及睡眠结构的改善则与药物治疗疗效相当。

Objective To investigate the effect of high frequency transcranial magnetic stimulation on depression and sleep quality in poststroke depression patients with insomnia. Methods 63 patients with PSD were randomly divided into observation group (n=32)and control group (n=31). Both groups were treated by 10~20 mg escitalopram citalopram for 4 weeks. The patients in observation group also accepted 10 Hz rTMS 10 times (i.e., as a course), while the patients in control group were treated by sham stimulation. At the baseline and 4th week, the 17-item Hamilton depression scale (17-HAMD), Pittsburgh Sleep Quality Index (PSQI)and polysomnography (PSG)were evaluated. Results The sleep parameters, PSQI scores and HAMD scores among two groups had no significant difference at baseline. After 4 weeks treatment, the HAMD score, PSQI score, total sleep duration, sleep efficiency and proportion of rapid eye movement sleep in both groups were improved compared with those before treatment. The descend range of HAMD score in observation group was larger than that in control group (t=2.590,P=0.012), while the descend range of PSQI scores(t=0.897,P=0.373)and the change of the sleep parameters in the two group had no obvious difference. Conclusion High frequency rTMS has better curative effect than antidepressant therapy on depressive symptoms of PSD,while there was no difference on the effect to improve the sleep quality and sleep structure of PSD between these two treatments.

       目的   本研究以脑卒中患者为研究对象，通过二代Illumina高通量测序平台对患者的粪便标本进行微生物群落多样性测序。选择物种丰度≥30%的24个门类（Phylum）作为肠道菌群的研究指标，进而研究肠道菌群与脑卒后抑郁（PSD）之间的相关关系。方法   以40位脑卒中患者的24个门类作为特征变量，抑郁组和对照组为二分类目标变量，建立以Logistic回归、随机森林、支持向量机和AdaBoost为基模型的Stacking分类模型。主成分分析方法作为该模型的特征选择方法选择恰当的主成分进行模型训练，通过二分类评价报告（precision、recall、f1-score）、ROC曲线和混淆矩阵等评价指标对其性能进行评价。结果   （1）通过差异性检验分析了两组（抑郁组和对照组）的基线一致（P＜0.05）；（2）从Stacking模型融合的角度定量分析了影响脑卒中后抑郁情绪的具体肠道菌群。研究结果可知，放线菌门、拟杆菌门、变形菌门和酸杆菌门在PSD患者中均增加（P＜0.001）；厚壁菌门，疣微菌门，绿弯菌门和软壁菌门在PSD患者中降低（P＜0.001）。结论   以上菌群是影响脑卒中后抑郁患者情绪的主要影响因素，因此，在临床上通过恰当干预肠道菌群的变化来调节脑卒中后抑郁患者的抑郁水平，这为脑卒中后抑郁情绪的诊断和治疗方案提供科学依据。

        Objective  In this study，patients with stroke were selected as the research object，and the microbial community diversity of patients’ stool samples was sequenced by the second-generation Illumina high-throughput sequencing platform．Twenty four phylum species with 30% species abundance were selected as indicators for the study of gut microbiota，and then the correlation between gut microbiota and post-stroke depression（PSD） was studied．Methods  Taking 24 categories of 40 stroke patients as characteristic variables，depression group and control group as dichotomous target variables，a stacking classification model based on Logistic regression，random forest，support vector machine and AdaBoost was established．As the feature selection method of the model，principal component analysis selects the appropriate principal components for model training，and evaluates its performance through dichotomous evaluation reports（precision，recall，f1 score），ROC curve and confusion matrix．Results  The baseline of the two groups（depression group and control group）was consistent（P＜0.05）through the difference test．From the perspective of stacking model fusion，the specific intestinal flora affecting post-stroke depression was quantitatively analyzed．The results showed that Actinobacteria，Bacteroidetes，Proteobacteria and Acidobacteria were significantly increased in PSD patients（P＜0.001），while Firmicutes，Verrucomicrobia，Chloroflexi and Tenericutes were significantly decreased in PSD patients（P＜0.001）．Conclusions  The above microbiota are the main factors affecting the mood of patients with post-stroke depression．Therefore，in clinical practice，we can adjust the depression level of patients with post-stroke depression by properly intervening the changes of intestinal microbiota，which provides a scientific basis for the diagnosis and treatment of PSD．