BioMed research international
-
The aim of this study was to directly compare the clinical outcomes of posterior lumbar interbody fusion (PLIF) and minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) in three-level lumbar spinal stenosis. This retrospective study involved a total of 60 patients with three-level degenerative lumbar spinal stenosis who underwent MIS-TLIF or PLIF from January 2010 to February 2012. Back and leg visual analog scale (VAS), Oswestry Disability Index (ODI), and Short Form-36 (SF-36) scale were used to assess the pain, disability, and health status before surgery and postoperatively. ⋯ However, significantly less blood loss and shorter hospital stay were observed in MIS-TLIF group (P < 0.05). Moreover, patients undergoing MIS-TLIF had significantly lower back VAS than those in PLIF group at 6-month follow-up (P < 0.05). Compared with PLIF, MIS-TLIF might be a prior option because of noninferior efficacy as well as merits of less blood loss and quicker recovery in treating three-level lumbar spinal stenosis.
-
To investigate whether P. acnes could induce disc degeneration and Modic changes when inoculated into the discs of rabbits. ⋯ Compared to S. aureus, the pathological change caused by P. acnes would be considered as Modic-I change and disc degeneration rather than a discitis.
-
The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of "vocabulary gap" and data sparseness and the unattainable automation process in feature extraction. ⋯ We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores.
-
The use of robotic technology in the surgical treatment of brain tumour promises increased precision and accuracy in the performance of surgery. Robotic manipulators may allow superior access to narrow surgical corridors compared to freehand or conventional neurosurgery. This paper reports values and ranges of tool-tissue interaction forces during the performance of glioma surgery using an MR compatible, image-guided neurosurgical robot called neuroArm. ⋯ Mean values of the peak forces varied in range of 1.27 N (anaplastic astrocytoma, WHO grade III) to 1.89 N (glioblastoma with oligodendroglial component, WHO grade IV). In some cases, ANOVA test failed to reject the null hypothesis of equality in means of the peak forces measured. However, we could not find a relationship between forces exerted to the pathological tissue and its size, type, or location.
-
The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. ⋯ We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.