Disposable electrochemical flow cells for catalytic adsorptive stripping voltammetry (CAdSV) at a bismuth film electrode (BiFE)

Catalytic adsorptive stripping voltammetry (CAdSV) has been demonstrated at a bismuth film electrode (BiFE) in an injection-moulded electrochemical micro-flow cell. The polystyrene three-electrode flow cell was fabricated with electrodes moulded from a conducting grade of polystyrene containing 40% carbon fibre, one of which was precoated with Ag to enable its use as an on-chip Ag/AgCl reference electrode. CAdSV of Co(II) and Ni(II) in the presence of dimethylglyoxime (DMG) with nitrite employed as the catalyst was performed in order to assess the performance of the flow cell with an in-line plated BiFE. The injection-moulded electrodes were found to be suitable substrates for the formation of BiFEs. Key parameters such as the plating solution matrix, plating flow rate, analysis flow rate, solution composition and square-wave parameters have been characterised and optimal conditions selected for successful and rapid analysis of Co(II) and Ni(II) at the ppb level. The analytical response was linear over the range 1 to 20 ppb and deoxygenation of the sample solution was not required. The successful coupling of a microfluidic flow cell with a BiFE, thereby forming a “mercury-free” AdSV flow analysis sensor, shows promise for industrial and in-the-field applications where inexpensive, compact, and robust instrumentation capable of low-volume analysis is required

O fra Bernardinu Splićaninu, priređivaču prvog izdanja hrvatskog lekcionara, ponovo!

The use of miniaturised isotachophoresis to allow the simultaneous determination of two inorganic selenium species has been investigated using a poly(methyl methacrylate) chip with a 44-mm-long, 200-μm-wide, 300-μm-deep separation channel. The miniaturised device included an integrated on-column, dual-electrode conductivity detector and was used in conjunction with a hydrodynamic fluid transport system. A simple electrolyte system has been developed which allowed the separation of selenium(IV) and selenium(VI) species to be made in under 210 s. The limits of detection were calculated to be 0.52 mg L−1 for selenium(IV) and 0.65 mg L−1 for selenium(VI). The method allowed the separation of the selenium species from a range of common anions including fluoride, nitrate, nitrite, phosphate, sulfate and sulfite

Regional in vivo transit time measurements of aortic pulse wave velocity in mice with high-field CMR at 17.6 Tesla

<p>Abstract</p> <p>Background</p> <p>Transgenic mouse models are increasingly used to study the pathophysiology of human cardiovascular diseases. The aortic pulse wave velocity (PWV) is an indirect measure for vascular stiffness and a marker for cardiovascular risk.</p> <p>Results</p> <p>This study presents a cardiovascular magnetic resonance (CMR) transit time (TT) method that allows the determination of the PWV in the descending murine aorta by analyzing blood flow waveforms. Systolic flow pulses were recorded with a temporal resolution of 1 ms applying phase velocity encoding. In a first step, the CMR method was validated by pressure waveform measurements on a pulsatile elastic vessel phantom. In a second step, the CMR method was applied to measure PWVs in a group of five eight-month-old apolipoprotein E deficient (ApoE^(-/-)) mice and an age matched group of four C57Bl/6J mice. The ApoE^(-/-)group had a higher mean PWV (PWV = 3.0 ± 0.6 m/s) than the C57Bl/6J group (PWV = 2.4 ± 0.4 m/s). The difference was statistically significant (p = 0.014).</p> <p>Conclusions</p> <p>The findings of this study demonstrate that high field CMR is applicable to non-invasively determine and distinguish PWVs in the arterial system of healthy and diseased groups of mice.</p

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses