腹腔镜下子宫双侧动脉阻断术联合清宫术治疗剖宫产瘢痕妊娠疗效分析

目的 探讨腹腔镜下子宫双侧动脉阻断术联合清宫术治疗剖宫产瘢痕妊娠疗效及对内分泌的影响.方法 选择2011年1月至2015年12月剖宫产瘢痕妊娠患者57例,其中A组32例患者给予超声引导下清宫术治疗,B组25例患者给予腹腔镜下子宫双侧动脉阻断术联合清宫术治疗,比较两组患者出血量、住院时间、月经复潮时间、术后人绒毛膜促性腺激素(hCG)降至正常时间,并发症情况及术前(T0)、术后第1d(T1)、第3d(T2)神经内分泌激素水平.结果 ①B组患者出血量低于A组,住院时间、月经复潮时间短于A组,比较差异有统计学意义(t出血量=31.85,k院时间=9.36,t月经复t潮时间=16.37,均P＜0.05);②B组患者并发症发生率为8.00％,低于A组的15.63％,比较差异有统计学意义(x2=9.35,P＜0.05);③B组T1、T2血清COR、β-EP、GLU水平低于A组(CORF交互=17.67,β-EPF交互=132.36,6LUF交互=155.38,均P＜0.05).结论 腹腔镜下子宫双侧动脉阻断术联合清宫术治疗剖宫产瘢痕妊娠微创优势明显,安全性高,对内分泌影响较轻.     

丹红注射液对急性心肌梗死PCI围手术期心功能和TIMI血流影响的Meta分析

目的:系统评价丹红注射液对急性心肌梗死(AMI)经皮冠状动脉介入治疗(PCI)围手术期心功能和心肌梗塞溶栓治疗(TIMI)血流分级的影响。方法:计算机检索CNKI,万方数据库,维普数据库,Pub Med,CBM,Web of Science,The Cochrane Library共7个数据库,全面采集在PCI围手术期应用丹红注射液治疗急性心梗的临床试验,采用Cochrane风险评价表进行文献质量评价,运用Revman 5.3软件进行Meta分析。结果:共纳入12个临床试验,包含1131例患者,其中丹红治疗组569例,对照组562例,结果显示在常规治疗的基础上加入丹红注射液治疗,患者的左室射血分数明显增高[均数差(MD)=6.62,95%可信区间(CI)(4.91,8.34),P<0.00001],TIMI分级3级患者明显增多[相对危险度(RR)=0.22,95%CI(0.12,0.41),P<0.00001],脑利钠肽水平明显降低[MD=-151.86,95%CI(-247.00,-56.72),P=0.002]。结论:丹红注射液可以提高急性心梗PCI围手术期心功能和增加TIMI血流的分级。     

基于多元统计分析的银杏叶提取物中有机酸类成分含量研究

目的建立HPLC法测定银杏叶提取物中9种小分子有机酸类成分莽草酸、儿茶素、表儿茶素、没食子酸、原儿茶酸、6-羟基犬尿喹啉酸、对羟基苯甲酸、对羟基肉桂酸、咖啡酸含量的方法,并结合多元统计分析方法比较不同厂家生产的银杏叶提取物间质量差异。方法采用Inertsil ODS-3 C_(18)(250 mm×4.6 mm,5μm)色谱柱;以乙腈-0.4%磷酸水溶液为流动相,梯度洗脱,波长切换测定(220 nm检测莽草酸、儿茶素、表儿茶素,254 nm检测没食子酸、原儿茶酸、6-羟基犬尿喹啉酸、对羟基苯甲酸,310 nm检测对羟基肉桂酸、咖啡酸),柱温40℃。结果不同厂家生产的银杏叶提取物中有机酸总含量有差异,厂家内部样品中各有机酸含量同样存在差异。经聚类分析、主成分分析、相关与回归分析等多元统计方法分析,筛选出儿茶素、没食子酸、原儿茶酸及6-羟基犬尿喹啉酸为体现样品质量差异的特征成分,同时成分间具有内在相关性。结论建立的方法操作简便、重复性好、结果可靠,可用于银杏叶提取物中有机酸类成分的质量评价。筛选出的儿茶素、没食子酸、原儿茶酸及6-羟基犬尿喹啉酸是体现质量差异的特征成分,又是具有内在关联的共性成分,为提升银杏叶提取物整体质量控制能力提供了依据,同时表明银杏叶提取物的提取工艺并不统一,企业内部工艺的稳定性也有待提高。     

Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation

BackgroundIn the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model.MethodsMicroarray data of rat gene expression profiles under accession number {"type":"entrez-geo","attrs":{"text":"GSE28545","term_id":"28545"}}GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba.ResultsThree hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes.ConclusionsThese key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.     

支持向量机法及其在中药研究中的应用

在中医药现代化进程中，有效地结合现代科学技术手段对传统中医药进行更为合理的分析是一个重要研究方向。计算机科学技术与人工智能算法在中医药研究领域已取得长足发展，尤其是分类算法所具有的快速鉴别和分析能力，使其在复杂的中医药研究体系中发挥举足轻重的作用。对支持向量机分类算法及其结合其他算法在解决中医药相关领域问题方面及成果进行综述，以相关智能算法为基础探讨中医药研究过程中的诸多难题，为推动中医药现代化进程提供支撑。     

某设备所致脉冲X射线辐射场测量与防护评价

目的 了解某设备实验条件下不同位置脉冲X射线电离辐射水平，提出适当的防护建议。方法 采用热释光测量方法，分别在设备舱周围不同方向不同距离布放热释光剂量计，累积一定数量脉冲辐射后进行测量；采用电离室型X、γ剂量率仪（FJ-347A）实时测量工作状态下不同距离处电离辐射剂量率水平。依据《电离辐射防护与辐射源安全基本标准》（GB18871—2002）规定的职业照射人员和公众个人剂量限值提出不同工作位辐射防护建议。结果 热释光剂量计累计接收3 000个脉冲辐射，设备舱外壁0.01～8.98 mGy，顶部0.01～15.67 mGy，距外壁1～12 m之间0.01～2.18 mGy，工作位0 mGy。工作状态下，X射线剂量率仪测得距设备舱外侧壁1～20 m之间空气比释动能率范围0.26～16 mGy/h。结论 热释光剂量计、电离室型剂量率仪测量结果基本一致，说明两种方法均可用于脉冲X射线测量；工作状态下设备舱外近距离处辐射剂量率较高，可通过采取防护措施或者限制人员工作量以满足辐射防护要求。     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.