目的:探讨肛肠疾病手术前后肛管直肠压力测定的应用。方法:将2018年5月-2019年5月在上海市松江区方塔中医医院及上海中医药大学附属曙光医院肛肠科行手术治疗的826例肛肠疾病患者作为研究对象,其中,选择性痔上黏膜吻合术246例、单纯外剥内扎术115例、外剥内扎结合内痔套扎术(Automatic Ligation of Hemorrhoids,RPH)153例、低位肛瘘切除术177例、高位肛瘘切开挂线术135例,分别于术前及术后1个月测定肛管直肠压力。结果:选择性痔上黏膜吻合术后直肠静息压、肛管静息压明显低于术前,肛管舒张压高于术前(P<0.05),但肛管最大收缩压与术前相比无明显差异(P>0.05);单纯外剥内扎术术后直肠静息压、肛管静息压明显低于术前,肛管舒张压、肛管最大收缩压明显高于术前(P<0.05);外剥内扎结合内痔套扎术术后直肠静息压、肛管静息压明显低于术前,肛管舒张压、肛管最大收缩压明显高于术前(P<0.05);低位肛瘘切除术术后直肠静息压、肛管静息压、肛管舒张压均高于术前(P<0.05),而肛管最大收缩压与术前相比无明显差异(P>0.05);高位肛瘘切开挂线术术后直肠静息压高于术前,肛管静息压、肛管舒张压低于术前(P<0.05),而与肛管最大收缩压术前相比无明显差异(P>0.05)。结论:肛肠疾病手术前后肛管直肠压力测定的应用效果显著,能准确判断手术效果及患者恢复情况,为医师的进一步诊治奠定了良好基础。 相似文献
In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.
For over three decades, there has been a continuing panzootic caused by a virulent variant avian paramyxovirus type 1 strain, the so-called pigeon paramyxovirus type 1. It is found primarily in racing pigeons, but it has also spread to wild birds and poultry. In this study, two pigeon paramyxovirus type 1 strains, SD12 and BJ13, obtained from diseased pigeons in China, were characterized. Phylogenetic analysis based on complete sequences allowed characterization of both strains as genotype VI, class II. Further phylogenetic analysis of a 374-nucleotide section of the fusion gene showed that SD12 fell into lineage VIbii-d and BJ13 into VIbii-f. The deduced amino acid sequence of the cleavage site of the fusion protein confirmed that both isolates contained the virulent motif 112K/RRQKR↓F117 at the cleavage site. Nevertheless, the values of intracerebral pathogenicity indices showed the SD12 isolate to be a velogenic strain and BJ13 isolate to be a mesogenic strain. The SD12 isolate was further investigated via clinical observation, RNA detection, histopathology and viral serology in experimentally infected 3-week-old chickens. It showed a mild pathological phenotype in chickens, with viral replication restricted to a few tissues. The molecular mechanism for the SD12 isolate to have a virulent motif but low levels of virulence for chickens requires further study. 相似文献