Acceleration of cardiac tissue simulation with graphic processing units

In this technical note we show the promise of using graphic processing units (GPUs) to accelerate simulations of electrical wave propagation in cardiac tissue, one of the more demanding computational problems in cardiology. We have found that the computational speed of two-dimensional (2D) tissue simulations with a single commercially available GPU is about 30 times faster than with a single 2.0 GHz Advanced Micro Devices (AMD) Opteron processor. We have also simulated wave conduction in the three-dimensional (3D) anatomic heart with GPUs where we found the computational speed with a single GPU is 1.6 times slower than with a 32-central processing unit (CPU) Opteron cluster. However, a cluster with two or four GPUs is faster than the CPU-based cluster. These results demonstrate that a commodity personal computer is able to perform a whole heart simulation of electrical wave conduction within times that enable the investigators to interact more easily with their simulations

Phylogenetic analysis of porcine parvoviruses from swine samples in China

<p>Abstract</p> <p>Background</p> <p>Porcine parvovirus (PPV) usually causes reproductive failure in sows. The objective of the present study was to analyze the phylogenetic distribution and perform molecular characterization of PPVs isolated in China, as well as to identify two field strains, LZ and JY. The data used in this study contained the available sequences for NS1 and VP2 from GenBank, as well as the two aforementioned Chinese strains.</p> <p>Results</p> <p>Phylogenetic analysis shows that the PPV sequences are divided into four groups. The early Chinese PPV isolates are Group I viruses, and nearly all of the later Chinese PPV isolates are Group II viruses. LZ belongs to group II, whereas the JY strain is a Group III virus. This is the first report on the isolation of a Group III virus in China. The detection of selective pressures on the PPV genome shows that the NS1 and VP2 genes are under purifying selection and positive selection, respectively. Moreover, the amino acids in the VP2 capsid are highly variable because of the positive selection.</p> <p>Conclusions</p> <p>Our study provides new molecular data on PPV strains in China, and emphasizes the importance of etiological studies of PPV in pigs.</p

Prostaglandin E1 Alleviates Cognitive Dysfunction in Chronic Cerebral Hypoperfusion Rats by Improving Hemodynamics

Compensatory vascular mechanisms can restore cerebral blood flow (CBF) but fail to protect against chronic cerebral hypoperfusion (CCH)-mediated neuronal damage and cognitive impairment. Prostaglandin E1 (PGE1) is known as a vasodilator to protect against ischemic injury in animal models, but its protective role in CCH remains unclear. To determine the effect of PGE1 on cerebral hemodynamics and cognitive functions in CCH, bilateral common carotid artery occlusion (BCCAO) was used to mimic CCH in rats, which were subsequently intravenously injected with PGE1 daily for 2 weeks. Magnetic resonance imaging, immunofluorescence staining and Morris water maze (MWM) were used to evaluate CBF, angiogenesis, and cognitive functions, respectively. We found that PGE1 treatment significantly restored CBF by enhancing vertebral artery dilation. In addition, PGE1 treatment increased the number of microvascular endothelial cells and neuronal cells in the hippocampus, and decreased the numbers of astrocyte and apoptotic cells. In the MWM test, we further showed that the escape latency of CCH rats was significantly reduced after PGE1 treatment. Our results suggest that PGE1 ameliorates cognitive dysfunction in CCH rats by enhancing CBF recovery, sustaining angiogenesis, and reducing astrocyte activation and neuronal loss

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe