Additional file 1: of Comparison of the clinical performance of the flexible laryngeal mask airway in pediatric patients under general anesthesia with or without a muscle relaxant: study protocol for a randomized controlled trial

SPIRIT checklist. (DOC 123 kb

Additional file 1: of Effects of C8 nerve root block during interscalene brachial plexus block on anesthesia of the posterior shoulder in patients undergoing arthroscopic shoulder surgery: study protocol for a prospective randomized parallel-group controlled trial

SPIRIT checklist. (DOC 122 kb

Source Apportionment of PM_2.5 at an Urban IMPROVE Site in Seattle, Washington

The multivariate receptor models Positive Matrix Factorization (PMF) and Unmix were used along with the EPA's Chemical Mass Balance model to deduce the sources of PM2.5 at a centrally located urban site in Seattle, WA. A total of 289 filter samples were obtained with an IMPROVE sampler from 1996 through 1999 and were analyzed for 31 particulate elements including temperature-resolved fractions of the particulate organic and elemental carbon. All three receptor models predicted that the major sources of PM2.5 were vegetative burning (including wood stoves), mobile sources, and secondary particle formation with lesser contributions from resuspended soil and sea spray. The PMF and Unmix models were able to resolve a fuel oil combustion source as well as distinguish between diesel emissions and other mobile sources. In addition, the average source contribution estimates via PMF and Unmix agreed well with an existing emissions inventory. Using the temperature-resolved organic and elemental carbon fractions provided in the IMPROVE protocol, rather than the total organic and elemental carbon, allowed the Unmix model to separate diesel from other mobile sources. The PMF model was able to do this without the additional carbon species, relying on selected trace elements to distinguish the various combustion sources

Blood pressure and lipid target adherence in Korean patients receiving angiotensin II receptor blockers/statin regimens

<p><b>Objectives:</b> Hypertension and dyslipidemia are important cardiovascular risk factors. Simultaneously controlling blood pressure (BP) and lipid levels is effective in preventing cardiovascular events and premature death. This study investigated the association between adherence to angiotensin II receptor blocker (ARB)/statin regimens and BP or low density lipoprotein-cholesterol (LDL-C) target attainment in Korean patients with concomitant hypertension and dyslipidemia.</p> <p><b>Methods:</b> In this retrospective, multicenter study, we collected case report forms (CRFs) of hypertensive patients with concomitant dyslipidemia who were prescribed an ARB/statin regimen between 1 April 2014 and 31 March 2015 from 51 outpatient clinics. A total of 672 and 609 patients were eventually included for statistical analyses of BP and LDL-C, respectively. Adherence was measured by medication possession ratio (MPR) for 6 months following the index date.</p> <p><b>Results:</b> The overall rates of attaining BP and LDL-C targets were 75.6% and 81.1%, respectively. The mean value of MPR for patients attaining target BP or LDL-C was significantly higher than that for those not attaining target BP or LDL-C. After adjustment for all covariates, increases in the quartiles of MPR were significantly associated with an increased probability of attaining target BP or LDL-C in all models (all <i>p</i>-trend <0.05). Attaining of BP control was significantly higher in quartiles 3 and 4 of MPR (MPR >0.95) than the lowest MPR (quartile 1), whereas attaining LDL-C target was associated with quartile 4 of MPR (MPR >0.97).</p> <p><b>Conclusion:</b> We identified a strong correlation between medication adherence and BP or LDL-C target achievement in Korean patients with concomitant hypertension and dyslipidemia. The adherence for reaching targets could be different between BP and LDL-C levels.</p

Sources of Fine Particles in a Rural Midwestern U.S. Area

Ambient PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) samples collected at a rural monitoring site in Bondville, IL on every third day using Interagency Monitoring of Protected Visual Environments (IMPROVE) sampler were analyzed through the application of the positive matrix factorization (PMF). The particulate carbon fractions were obtained from the thermal optical reflectance method that divides particulate carbon into four organic carbon, pyrolyzed organic carbon (OP), and three elemental carbon fractions. A total of 257 samples collected between March 2001 and May 2003 analyzed for 35 species were used and eight sources were identified:  summer-high secondary sulfate aerosol (40%), secondary nitrate aerosol (32%), gasoline vehicle (9%), OP-high secondary sulfate aerosol (7%), selenium-high secondary sulfate aerosol (4%), airborne soil (4%), aged sea salt (2%), and diesel emissions (2%). The compositional profiles for gasoline vehicle and diesel emissions are similar to those estimated in other U.S. areas. Backward trajectories indicate that the highly elevated airborne soil impacts were likely caused by Asian and Saharan dust storms. Potential source contribution function analyses show the potential source areas and pathways of secondary sulfate aerosols, especially the regional influences of the biogenic as well as anthropogenic secondary aerosol

Simultaneous imaging of brain tumor angiogenesis and invasion with μMRI.

<p>(a) FA map of a patient-derived, invasive primary glioma model. (b) Zoomed view of the hatched region in (a) showing two ROIs in the corpus callosum for which the FA was analyzed. (c) Histograms of the FA from the ROIs in (b), wherein one can see that the FAs from ROI-1 are shifted toward lower values than those from ROI-2. (d) Histology (H&E) from the same region as in (b) in which one can see the white matter tract (WM) being infiltrated by a tuft of tumor cells (I). The tumor margin (T) is also visible in (d). (e) Visualization of the DTI tensors as 3D ellipsoid glyphs for one μMRI slice, wherein each ellipsoid is scaled according the values of the three principal eigen-vectors and color coded according to the FA. The invasive primary tumor (hatched outline) is identifiable by its lower FA in contrast to the contralateral brain. (f) Visualization of the 3D vasculature for the whole brain. Tumor vasculature (hatched outline) is dense and chaotic relative to that of the contralateral brain. (g) The image in (e) overlaid with that in (f) allows us to simultaneously assess the interaction between brain tumor angiogenesis and the effects of tumor invasion on the integrity of white matter tracts. The tumor ROI is highlighted by a hatched outline.</p

Bridging macroscale and microscale MRI.

<p>(a) In vivo macrovascular CBV (ΔR₂*) map. (b) Co-registered <i>ex vivo</i> fractional blood volume (FV) map obtained from μMRI. The tumor ROI is highlighted by hatched lines in each panel and FV ranges from 0 to 1. (c) Histograms showing the relative distribution of the ΔR₂* between tumor and contralateral ROIs. (d) Histograms showing the relative distribution of the FV between tumor and contralateral ROIs. Tumor blood volume is elevated relative to the contralateral brain across these “multi-scale” data. (e) 2D histograms of the macrovascular CBV measured <i>in vivo</i> versus the fractional blood volume assessed <i>ex vivo</i>. These data further demonstrate the utility of multi-scale imaging of brain tumor vascularization.</p

Multi-scale MRI of a 9L brain tumor model.

<p>(a) In vivo T₂-weighted MRI at the ‘systemic’ scale (∼150 µm); ex vivo μMRI at two ‘intermediate’ scales: (b) ∼60 µm, and (c) ∼30 µm. (d) Ultra-high resolution vascular μMRI image in which vessels have been segmented into tumor vessels (gold) and normal vessels (red). One can clearly visualize the abnormal tumor vessel architecture and changes in vessel morphology at the tumor-host tissue interface (arrows).</p

Spatial Variability of Fine Particle Mass, Components, and Source Contributions during the Regional Air Pollution Study in St. Louis

Community time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentra tions to human health effects. If the day-to-day variations in 24-hour integrated concentrations differ substantially across an urban area (i.e., daily measurements at monitors at different locations are not highly correlated), then there is a significant potential for exposure misclassification in community time-series epidemiology. If the annual average concentration differs across an urban area, then there is a potential for exposure misclassification in epidemiologic studies that use annual averages (or multi-year averages) as an index of exposure across different cities. The spatial variability in PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), its elemental components, and the contributions from each source category at 10 monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study (RAPS) based on the Regional Air Monitoring System (RAMS) conducted between 1975 and 1977. Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. For example, the contributions from incinerators show a difference of a factor of 12.5 between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and contributions from source categories can be highly heterogeneous within a given airshed and thus, there is the potential for exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures

Ultra-high resolution 3D μMRI and “zonal” analyses of the neurovasculature.

<p>(a) 3D rendering of the neurovasculature in a non-invasive, 9L tumor bearing mouse brain acquired using ultra-high resolution (30 µm×30 µm×30 µm) μMRI. The vasculature has been color coded into three different “zones”: normal vessels (blue), tumor vessels (red) and vessels at the tumor-brain interface or transition zone (green). The transition-zone or tumor-brain tissue interface is crucial to understanding both, brain tumor angiogenesis and invasion. The radius and length of every individual vessel segment was measured in each zone. (b) Box plot of the average vessel length in each zone, wherein the width of each box includes 75% of the measured lengths and the median length is indicated by a horizontal line in each box. In addition, the radius of every vessel segment is plotted for each zone, with the color and size of each symbol proportional to the vessel radius. The normal zone exhibited significantly longer vessel segments compare to the transition (p = 0.002) and tumor zones (p<0.001), respectively. At this tumor stage, vessel radii were similar between the tumor and normal zones. These data demonstrate our ability to characterize the neurovasculature in physiologically relevant “zones”, and could provide new insight into the relationship between brain tumor angiogenesis and invasion. (c) T₂-weighted μMRI slice through a 9L brain tumor (gold rendering) bearing brain. (d) 3D overlay of the neurovasculature acquired using ultra-high resolution μMRI. (e) 3D DTI image showing reorganization of the fibers of the anterior commissure (<i>ac</i>) and internal capsule (<i>ic</i>) around the tumor. (f) Overlay of (d) and (e) illustrating simultaneous changes in vascular and white matter structures.</p