The objective of the present study was to evaluate the clinical, radiological, and functional outcomes of a subscapularis transthoracic surgical approach and a posterolateral surgical approach with debridement, bone graft fusion, and internal fixation for the treatment of upper thoracic tuberculosis.There is currently debate over the best surgical approach for the treatment of upper thoracic tuberculosis. Traditionally, the subscapularis transthoracic approach has been preferred; however, the posterolateral approach has gained popularity in the past few years.A prospective, consecutive cohort of 43 upper thoracic tuberculosis patients with a mean age of 39 years (range: 20–52 years) was followed up for a minimum of 12 months (range: 12–60 months). Patients were randomly divided into 2 groups. Group A (n = 21) was treated by the subscapularis transthoracic approach and group B (n = 22) was treated by the posterolateral approach. All cases were evaluated for clinical, radiological, and functional outcomes. Intraoperative blood loss, operative duration, intraoperative and postoperative complications, hospital stay, the cure rate, fusion time, and the Frankel scale were used for clinical and functional evaluation, whereas the kyphosis angle was used for radiological evaluation.Grafted bones were fused by 10 months in all cases. There was no statistically significant difference between groups before surgery in terms of gender, age, segmental tuberculosis, erythrocyte sedimentation rate (ESR), Frankel scale, or Cobb''s angle (P > 0.05). The average operative duration for Group B was lower than that of Group A. There were no significant differences in intraoperative blood loss, intraoperative and postoperative complications, hospital stay, grafted bone fusion time, or cure rate between groups (P > 0.05). The Cobb''s angle correction rate for group B (68.5%) was significantly better than that of group A (30.9%). The neurological score showed significant postoperative improvement in both groups, with no significant difference between the groups.The subscapularis transthoracic approach and the posterolateral approach with debridement, bone graft fusion, and internal fixation are both sufficient and satisfactory for the surgical treatment of upper thoracic tuberculosis. However, the posterolateral approach is superior to the subscapularis transthoracic approach in terms of surgical trauma, operative time, and kyphosis correction. 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.
