Functional outcomes after stabilization with Dynesys in patients with spinal degenerative diseases

Objective: To know the impact of the Dynesys system on the functional outcomes in patients with spinal degenerative diseases. Summary of background data: Dynesys system has been proposed as an alternative to vertebral fusion for several spinal degenerative diseases. The fact that it has been used in people with different diagnosis criteria using different tools to measure clinical outcomes makes very difficult unifying the results available nowadays. Methods: The data base of Medlars Online International Literature (MEDLINE) via PubMed©, EMBASE©, and the Cochrane Library Plus were reviewed in search of all the studies published until November 2012 in which an operation with Dynesys in patients with spinal degenerative diseases and an evaluation of the results by an analysis of functional outcomes had taken place. No limits were used to article type, date of publication or language. Results: A total of 134 articles were found, 26 of which fulfilled the inclusion criteria after being assessed by two reviewers. All of them were case series, except for a multicenter randomized clinical trial (RCT) and a prospective case-control study. The selected articles made a total of 1507 cases. The most frequent diagnosis were lumbar spinal canal stenosis (LSCS), degenerative disc disease (DDD), degenerative spondylolisthesis (DS) and lumbar degenerative scoliosis (LDS). In cases of lumbar spinal canal stenosis Dynesys was associated to surgical decompression. Several tools to measure the functional disability and general health status were found. Oswestry Disability Index (ODI), the ODI Korean version (K-Odi), Prolo, Sf-36, Sf-12, Roland-Morris disability questionnaire (RMDQ), and the pain Visual Analogue Scale (VAS) were the most used. They showed positive results in all cases series reviewed. In most studies the ODI decreased about 25% (e.g. from a score of 85% to 60%). Better results when dynamic fusion was combined with nerve root decompression were found. Functional outcomes and leg pain scores with Dynesys were statistically non-inferior to posterolateral spinal fusion using autogenous bone. When Dynesys and decompression was compared with posterior interbody lumbar fixation (PLIF) and decompression, differences in ODI and VAS were not statistically significant. Conclusions: In patients with spinal degenerative diseases due to degenerative disc disorders, spinal canal stenosis and degenerative spondylolisthesis, surgery with Dynesys and decompression improves functional outcomes, decreases disability, and reduces back and leg pain. More studies are needed to conclude that dynamic stabilization is better than posterolateral and posterior interbody lumbar fusion. Studies comparing Dynesys with decompression against decompression alone should be done in order to isolate the effect of the dynamic stabilization.

Identification of yeast genes that confer resistance to chitosan oligosaccharide (COS) using chemogenomics

Background: Chitosan oligosaccharide (COS), a deacetylated derivative of chitin, is an abundant, and renewable natural polymer. COS has higher antimicrobial properties than chitosan and is presumed to act by disrupting/permeabilizing the cell membranes of bacteria, yeast and fungi. COS is relatively non-toxic to mammals. By identifying the molecular and genetic targets of COS, we hope to gain a better understanding of the antifungal mode of action of COS. Results: Three different chemogenomic fitness assays, haploinsufficiency (HIP), homozygous deletion (HOP), and multicopy suppression (MSP) profiling were combined with a transcriptomic analysis to gain insight in to the mode of action and mechanisms of resistance to chitosan oligosaccharides. The fitness assays identified 39 yeast deletion strains sensitive to COS and 21 suppressors of COS sensitivity. The genes identified are involved in processes such as RNA biology (transcription, translation and regulatory mechanisms), membrane functions (e.g. signalling, transport and targeting), membrane structural components, cell division, and proteasome processes. The transcriptomes of control wild type and 5 suppressor strains overexpressing ARL1, BCK2, ERG24, MSG5, or RBA50, were analyzed in the presence and absence of COS. Some of the up-regulated transcripts in the suppressor overexpressing strains exposed to COS included genes involved in transcription, cell cycle, stress response and the Ras signal transduction pathway. Down-regulated transcripts included those encoding protein folding components and respiratory chain proteins. The COS-induced transcriptional response is distinct from previously described environmental stress responses (i.e. thermal, salt, osmotic and oxidative stress) and pre-treatment with these well characterized environmental stressors provided little or any resistance to COS. Conclusions: Overexpression of the ARL1 gene, a member of the Ras superfamily that regulates membrane trafficking, provides protection against COS-induced cell membrane permeability and damage. We found that the ARL1 COS-resistant over-expression strain was as sensitive to Amphotericin B, Fluconazole and Terbinafine as the wild type cells and that when COS and Fluconazole are used in combination they act in a synergistic fashion. The gene targets of COS identified in this study indicate that COS’s mechanism of action is different from other commonly studied fungicides that target membranes, suggesting that COS may be an effective fungicide for drug-resistant fungal pathogens.

Real-time 3D semi-local surface patch extraction using GPGPU

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.