Quantitative signal intensity in Fluid-Attenuated Inversion Recovery and treatment effect in the WAKE-UP trial

Background and Purpose: Relative signal intensity of acute ischemic stroke lesions in fluid-attenuated inversion recovery (fluid-attenuated inversion recovery relative signal intensity [FLAIR-rSI]) magnetic resonance imaging is associated with time elapsed since stroke onset with higher intensities signifying longer time intervals. In the randomized controlled WAKE-UP trial (Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke Trial), intravenous alteplase was effective in patients with unknown onset stroke selected by visual assessment of diffusion weighted imaging fluid-attenuated inversion recovery mismatch, that is, in those with no marked fluid-attenuated inversion recovery hyperintensity in the region of the acute diffusion weighted imaging lesion. In this post hoc analysis, we investigated whether quantitatively measured FLAIR-rSI modifies treatment effect of intravenous alteplase. Methods: FLAIR-rSI of stroke lesions was measured relative to signal intensity in a mirrored region in the contralesional hemisphere. The relationship between FLAIR-rSI and treatment effect on functional outcome assessed by the modified Rankin Scale (mRS) after 90 days was analyzed by binary logistic regression using different end points, that is, favorable outcome defined as mRS score of 0 to 1, independent outcome defined as mRS score of 0 to 2, ordinal analysis of mRS scores (shift analysis). All models were adjusted for National Institutes of Health Stroke Scale at symptom onset and stroke lesion volume. Results: FLAIR-rSI was successfully quantified in stroke lesions in 433 patients (86% of 503 patients included in WAKE-UP). Mean FLAIR-rSI was 1.06 (SD, 0.09). Interaction of FLAIR-rSI and treatment effect was not significant for mRS score of 0 to 1 (P=0.169) and shift analysis (P=0.086) but reached significance for mRS score of 0 to 2 (P=0.004). We observed a smooth continuing trend of decreasing treatment effects in relation to clinical end points with increasing FLAIR-rSI. Conclusions: In patients in whom no marked parenchymal fluid-attenuated inversion recovery hyperintensity was detected by visual judgement in the WAKE-UP trial, higher FLAIR-rSI of diffusion weighted imaging lesions was associated with decreased treatment effects of intravenous thrombolysis. This parallels the known association of treatment effect and elapsing time of stroke onset

An organelle-specific protein landscape identifies novel diseases and molecular mechanisms

Contains fulltext : 158967.pdf (publisher's version ) (Open Access)Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine

Grid

Earth Observation (EO) applications typically deal with the processing of large data quantities. When looking at the future, new high-resolution sensors, either multi-, super- or hyperspectral, will lead to even higher data quantities to be processed. A Grid service is needed to make sure such large amounts of data can be handled efficiently. Grids use the resources of many separate computers connected by a network (usually the Internet) to solve large-scale computation problems The idea behind the recent Grid technology for EO applications is to move the processing algorithms to the data instead of having to move large amounts of data to the user’s processing site ( = time consuming). The processing is done on computers in the data center, which have direct (fast) access to the data. Only the final results need to be transported over the Internet to the end-user. The data center of today evolves to being a processing center. The users will also benefit from the data storage offered by the Grid infrastructure