Introduction:
Minimally invasive transforaminal lumbar interbody fusion has proven effectiveness in treating spondylolisthesis, but there were few reports applying the technique from scarce resourcing developing countries. In this study, the authors report the results and share our experience of minimally invasive spinal transforaminal lumbar interbody fusion in spondylolisthesis treatment in Vietnamese patients.
Materials and methods:
In this study, the authors enroled 92 patients diagnosed with single-level, grade I or grade II lumbar spondylolisthesis from January 2019 to October 2022.
Results:
The median age in our study was 47.79±12.61 (range 15–75), the male/female ratio was 1/2.3. The mean disease duration was 28.57 months. Conventional X-ray images showed that there were 74 patients (80.43%) with spondylolisthesis grade I, 18 patients (19.57%) with grade II. Spondylolisthesis occured mainly in L4–L5 with 53 patients (57.61%). The isthmic sign was recorded in 16 patients (31.4%). The mean blood loss was 149.46 ml. Patients walked on average of 3.22 days after surgery. VAS score reduced significantly in both back and leg. Spinal function improved significantly with a preoperative Owestry Disability Index of 48.18% decrease to 15.18% 12 months after surgery. The surgical results were good and excellent at 95.00% after 12 months of surgery according to Macnab scale. The fusion rate reached 97.50%.
Conclusions:
The results of this Macnab's classification study show that minimally invasive spinal transforaminal lumbar interbody fusion is an effective treatment for spondylolisthesis with less pain, less blood loss after surgery, and high fusion rate.
With a high accuracy, the Smith-Waterman local sequence alignment algorithm requires a very large amount of memory and computation, making implementations on common computing systems become less practical. In this paper, we present swGPUCluster an implementation of the Smith-Waterman algorithm on a cluster equipped with NVIDIA GPU graphics cards (called a GPU cluster) . Our test was performed on a cluster of two nodes, one node is equipped with a dual graphics card NVIDIA GeForce GTX 295, a Tesla C1060 card, and the remaining node is equipped with 2 dual graphics cards NVIDIA GeForce GTX 295. Results show that the performance has increased significantly compared with the previous best implementations such as SWPS3 or CUDASW++. The performance of swGPUCluster has increased along with the lengths of query sequences, from 37.328 GCUPS to 46.706 GCUPS. These results demonstrate the great computing power of graphics cards and their high applicability in solving bioinformatics problems.
The Smith-Waterman algorithm is a common local sequence alignment method which gives a high accuracy. However, it needs a high capacity of computation and a large amount of storage memory, so implementations based on common computing systems are impractical. Here, we present our implementation of the Smith-Waterman algorithm on a cluster including graphics cards (GPU cluster) -swGPUCluster. The algorithm implementation is tested on a cluster of two nodes: a node is equipped with two dual graphics cards NVIDIA GeForce GTX 295, the other node includes a dual graphics cards NVIDIA GeForce 295 and a Tesla C1060 card. Depending on the length of query sequences, the swGPUCluster performance increases from 37.33 GCUPS to 46.71 GCUPS. This result demonstrates the great computing power of GPUs and their high applicability in the bioinformatics field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.