BACKGROUNDIncreased epicardial fat is known to be associated with the presence and chronicity of atrial fibrillation (AF). Free fatty acids (FFAs) are major components of epicardial fat; however, their potential association with AF in ischemic stroke has not been investigated. We aimed to assess the performance of echocardiographic epicardial fat thickness (EFT) and plasma FFA level in identifying patients with ischemic stroke and AF.METHODSWe enrolled a total of 214 consecutive patients (mean age, 66.8 ± 12.3 years; 39.7% women) diagnosed with acute ischemic stroke between March 2011 and June 2014. The patients were divided into two groups: ischemic stroke with AF (n = 35, 16.4%) and ischemic stroke without AF (n = 179, 83.6%).RESULTSThe ischemic stroke with AF group showed significantly higher serum FFA level (1379.7 ± 717.5 vs. 757.8 ± 520.5 uEq/L, p < 0.0001) and EFT (6.5 ± 1.2 vs. 5.3 ± 1.2 mm, p < 0.001) than the group without AF. Multivariable logistic regression analysis demonstrated that age (odds ratio [OR], 1.112), serum FFA level (OR, 1.002), and EFT (OR, 1.740) were independently associated with the ischemic stroke group with AF. EFT and FFA significantly improved the goodness-of-fit and discriminability of the simple regression model including age as a covariate (log likelihood difference, 21.35; p < 0.001; c-index difference, 17.9%; p < 0.001).CONCLUSIONSHigh EFT and serum FFA level were associated with ischemic stroke in patients with AF. Echocardiographic EFT and serum FFA level can play a significant role in identifying ischemic stroke with AF.

The ability to predict the efficacy of molecularly targeted therapies for non-small cell lung cancer (NSCLC) for an individual patient remains problematic. The purpose of this study was to identify, using a refined "coexpression extrapolation (COXEN)" algorithm with a continuous spectrum of drug activity, tumor biomarkers that predict drug sensitivity and therapeutic efficacy in NSCLC to Vorinostat, a histone deacetylase inhibitor, and Velcade, a proteasome inhibitor. Using our refined COXEN algorithm, biomarker prediction models were discovered and trained for Vorinostat and Velcade based on the in vitro drug activity profiles of nine NSCLC cell lines (NCI-9). Independently, a panel of 40 NSCLC cell lines (UVA-40) were treated with Vorinostat or Velcade to obtain 50% growth inhibition values. Genome-wide expression profiles for both the NCI-9 and UVA-40 cell lines were determined using the Affymetrix HG-U133A platform. Modeling generated multigene expression signatures for Vorinostat (45-gene; P = 0.002) and Velcade (15-gene; P = 0.0002), with one overlapping gene (CFLAR). Examination of Vorinostat gene ontogeny revealed a predilection for cellular replication and death, whereas that of Velcade suggested involvement in cellular development and carcinogenesis. Multivariate regression modeling of the refined COXEN scores significantly predicted the activity of combination therapy in NSCLC cells (P = 0.007). Through the refinement of the COXEN algorithm, we provide an in silico method to generate biomarkers that predict tumor sensitivity to molecularly targeted therapies. Use of this refined COXEN method has significant implications for the a priori examination of targeted therapies to more effectively streamline subsequent clinical trial design and cost. Mol Cancer Ther; 9(10); 2834-43. ©2010 AACR.

Electromagnetic surface waves propagating on the plane interface (x=0) between the electron–positron plasma and vacuum are investigated by the specular reflection procedure. The transverse electromagnetic modes are studied in terms of dispersion relation both in the presence and absence of an applied magnetic field. The analytic modes for some limiting cases are derived and discussed with the aid of some numerical analysis. In the presence of an applied magnetic field (B0=B0ŷ) directed perpendicular both to the interface normal and the wave vector, the cold electromagnetic surface wave dispersion relation shows that possible modes appear only when the frequency (ω) and the wave vector (k) satisfy the condition Ω2&lt;ω2&lt;Ω2+ωp2 and c2k2&gt;Ω2 (Ω is cyclotron frequency).

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Gene expression profiling technique now enables scientists to obtain a genome-wide picture of cellular functions on various human disease mechanisms which has also proven to be extremely valuable in forecasting patients' prognosis and therapeutic responses. A wide range of multivariate techniques have been employed in biomedical applications on such expression profiling data in order to identify expression biomarkers that are highly associated with patients' clinical outcome and to train multi-gene prediction models that can forecast various human disease outcome and drug toxicities. We provide here a brief overview on some of these approaches, succinctly summarizing relevant basic concepts, statistical algorithms, and several practical applications. We also introduce our recent in vitro molecular expression-based algorithm, the so-called COXEN technique, which uses specialized gene profile signatures as a Rosetta Stone for translating the information between two different biological systems or populations.