No abstract

The treatment of moyamoya disease (MMD) is controversial and often depends on the doctor's experience. In addition, the choice of surgical procedure to treat MMD can differ in many ways. In this study, we performed a meta-analysis to determine whether surgical treatment of MMD is superior to conservative treatment and to provide evidence for the selection of an appropriate surgical treatment.The human case–control studies regarding the association of MMD treatment were systematically identified through online databases (PubMed, Web of Science, Elsevier Science Direct, and Springer Link). Inclusion and exclusion criteria were defined for the eligible studies. The fixed-effects model was performed when homogeneity was indicated. Alternatively, the random-effects model was utilized.This meta-analysis included 16 studies. Surgical treatment significantly reduced the risk of stroke (odds ratio (OR) of 0.17, 95% confidence interval (CI), 0.12–0.26, P < 0.01). A subgroup analysis showed that surgical treatment was more beneficial to hemorrhagic MMD (OR of 0.23, 95% CI, 0.15–0.38, P < 0.01), but there was no significant difference between surgical treatment and conservative treatment on ischemic MMD treatment (OR of 0.45, 95% CI, 0.15–1.29, P = 0.14). Further analysis indicated that compared to direct bypass surgery, indirect bypass surgery had a lower efficacy on secondary stroke risk reduction (OR of 1.79, 95% CI, 1.14–2.82, P = 0.01), while no significant difference was detected for perioperative complications.Surgery is an effective treatment for symptomatic MMD patients, and direct bypass surgery may bring more benefits for these patients.

The development of algorithms for aircraft robust dynamic optimization considering uncertainties (for example, trajectory optimization) is relatively limited compared to aircraft robust static optimization (for example, configuration shape optimization). In this paper, an approach for dynamic optimization considering uncertainties is developed and applied to robust aircraft trajectory optimization. In the present approach, the nonintrusive polynomial chaos expansion scheme is employed to convert a robust trajectory optimization problem with stochastic ordinary differential equations into an equivalent deterministic trajectory optimization problem with deterministic ordinary differential equations. Two computational strategies for trajectory optimization considering uncertainties are compared. The performance of the developed method is studied by considering a classical deterministic trajectory optimization problem of supersonic aircraft short-time climb with uncertainties in the aerodynamic data. Detailed numerical studies are presented to illustrate the computational features of the proposed approach.

We aim to estimate multiple networks in the presence of sample heterogeneity, where the independent samples (i.e. observations) may come from different and unknown populations or distributions. Specifically, we consider penalized estimation of multiple precision matrices in the framework of a Gaussian mixture model. A major innovation is to take advantage of the commonalities across the multiple precision matrices through possibly nonconvex fusion regularization, which for example makes it possible to achieve simultaneous discovery of unknown disease subtypes and detection of differential gene (dys)regulations in functional genomics. We embed in the EM algorithm one of two recently proposed methods for estimating multiple precision matrices in Gaussian graphical models. We demonstrate the feasibility and potential usefulness of the proposed methods in an application to glioblastoma subtype discovery and differential gene network analysis with a microarray gene expression data set. We also conduct realistic simulation studies to evaluate and compare the performance of various methods.

BackgroundHigh-mobility group box 1 (HMGB1), originally described as a nuclear protein that binds to and modifies DNA, is now regarded as a central mediator of inflammation by acting as a cytokine. However, the association of HMGB1 in the peripheral blood with disease outcome and cerebrovasospasm has not been examined in patients with aneurysmal subarachnoid hemorrhage.MethodsIn this study, 303 consecutive patients were included. Upon admission, plasma HMGB1 levels were measured by ELISA. The end points were mortality after 1 year, in-hospital mortality, cerebrovasospasm and poor functional outcome (Glasgow Outcome Scale score of 1 to 3) after 1 year.ResultsUpon admission, the plasma HMGB1 level in patients was statistically significantly higher than that in healthy controls. A multivariate analysis showed that the plasma HMGB1 level was an independent predictor of poor functional outcome and mortality after 1 year, in-hospital mortality and cerebrovasospasm. A receiver operating characteristic curve showed that plasma HMGB1 level on admission statistically significantly predicted poor functional outcome and mortality after 1 year, in-hospital mortality and cerebrovasospasm of patients. The area under the curve of the HMGB1 concentration was similar to those of World Federation of Neurological Surgeons (WFNS) score and modified Fisher score for the prediction of poor functional outcome and mortality after 1 year, and in-hospital mortality, but not for the prediction of cerebrovasospasm. In a combined logistic-regression model, HMGB1 improved the area under the curve of WFNS score and modified Fisher score for the prediction of poor functional outcome after 1 year, but not for the prediction of mortality after 1 year, in-hospital mortality, or cerebrovasospasm.ConclusionsHMGB1 level is a useful, complementary tool to predict functional outcome and mortality after aneurysmal subarachnoid hemorrhage. However, HMGB1 determination does not add to the accuracy of prediction of the clinical outcomes.