(J Am Vet Med Assoc 2010;236:674-676)”
“Miniaturization
Selleckchem GSK126 in biological analyses has several advantages, such as sample volume reduction and fast response time. The integration of miniaturized biosensors within lab-on-a-chip setups under flow conditions is highly desirable, not only because it simplifies process handling but also because measurements become more robust and operator-independent. In this work, we study the integration of flow amperometric biosensors within a microfluidic platform when analyte concentration is indirectly measured. As a case study, we used a platinum miniaturized glucose biosensor, where glucose is enzymatically converted to H2O2 that is oxidized at the electrode. The experimental results produced are strongly coupled to a theoretical analysis of fluid dynamic conditions affecting the electrochemical response of the sensor. We verified that the learn more choice of the inlet flow rate is a critical parameter in flow biosensors, because it affects both glucose and H2O2 transport, to and from the electrode. We identify optimal flow rate conditions for accurate sensing at high time resolution. A dimensionless theoretical analysis allows the extension of the results to other sensing systems according to fluid dynamic similarity principles. Furthermore, we developed a microfluidic design that connects a sampling unit to the biosensor, in order to decouple the sampling flow rate
from that of the actual measurement. (C) 2012 American Institute of Physics. [http://dx.doi.org.elibrary.einstein.yu.edu/10.1063/1.4705368]“
“Background: Tariquidar supplier Analysis
of variance (ANOVA), change-score analysis (CSA) and analysis of covariance (ANCOVA) respond differently to baseline imbalance in randomized controlled trials. However, no empirical studies appear to have quantified the differential bias and precision of estimates derived from these methods of analysis, and their relative statistical power, in relation to combinations of levels of key trial characteristics. This simulation study therefore examined the relative bias, precision and statistical power of these three analyses using simulated trial data.
Methods: 126 hypothetical trial scenarios were evaluated (126 000 datasets), each with continuous data simulated by using a combination of levels of: treatment effect; pretest-posttest correlation; direction and magnitude of baseline imbalance. The bias, precision and power of each method of analysis were calculated for each scenario.
Results: Compared to the unbiased estimates produced by ANCOVA, both ANOVA and CSA are subject to bias, in relation to pretest-posttest correlation and the direction of baseline imbalance. Additionally, ANOVA and CSA are less precise than ANCOVA, especially when pretest-posttest correlation >= 0.3. When groups are balanced at baseline, ANCOVA is at least as powerful as the other analyses.