[Three-column reconstruction via posterior approach for the treatment of unstable thoracolumbar fracture accompanied by posterior column injury].

OBJECTIVE: To investigate clinical effects of three-column reconstruction via single posterior approach for the treatment of unstable thoracolumbar fractures accompanied by posterior column injury. METHODS: From December 2008 to May 2010,three-column reconstruction via posterior approach was implemented to 21 patients with unstable thoracolumbar fractures accompanied by posterior column injuries. There were 13 males and 8 females, ranging in age from 23 to 54 years old(averaged,35.5 years old). Injured vertebrae: 1 patient had injury in T11, 4 patients had injuries in T12, 8 patients had injuries in L1, 5 patients had injuries in L2, 3 patients had injuries in L3. The Cobb angle was (25.34 +/- 3.42) degrees. The operation time,blood loss during operation, Cobb angle and the bony fusion were observed. RESULTS: Twenty-one patients were followed up, and the duration ranged from 24 to 27 years old, with an average of 25.6 months. The operation time ranged from 135 to 275 min, with a mean of 185 min. The blood loss during operation ranged from 700 to 1 650 ml (averaged, 870 ml). All the patients had complete decompression. Postoperative Cobb angle was (4.01 +/- 2.03) degrees, and (4.34 +/- 2.38) degrees at the latest follow-up. All the patients got bony fusion. CONCLUSION: To the patients with unstable thoracolumbar fractures accompanied by posterior column injuries, three-column reconstruction via single posterior approach has both anterior approach and posterior approach advantages, which can obtain excellent clinical outcomes.

Inference of gene regulatory subnetworks from time course gene expression data.

BACKGROUND: Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks. RESULTS: We propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and can promisingly be applied to large-scale GRNs. Furthermore, we present the efficient Block PCA method for searching communities in GRNs. CONCLUSIONS: The NCI method is effective in identifying multiple subnetworks in a large-scale GRN. With the splitting algorithm, the Block PCA method shows a promosing attempt for exploring communities in a large-scale GRN.