The role of miRNA in diagnostics

Whilst the promise of RNA interference (RNAi) continues to expand in the therapeutic environment, the utility of the endogenous RNAi mechanism, microRNAs (miRNA), in diagnostics has been proven with the successful commercialization of a biopsy assay for the identification of cancer of unknown primary origin (CUP). The implications and consequences in personalized medicine arising from this highly sensitive and specific approach are substantial, however, the true potential is only beginning to be understood, as reports of disease-specific miRNA signatures in circulating lymphocytes, blood plasma/ serum, lung sputum, saliva and urine have been described, even in diseases where no diagnostic tools exist. This review summarizes the various approaches in miRNA biomarker discovery in addition to key findings in the field with the highest potential for clinical development

Low Complexity All-Pass Based Polyphase Decimation Filters for ECG Monitoring

This paper presents a low complexity high efficiency decimation filter which can be employed in EletroCardioGram (ECG) acquisition systems. The decimation filter with a decimation ratio of 128 works along with a third order sigma delta modulator. It is designed in four stages to reduce cost and power consumption. The work reported here provides an efficient approach for the decimation process for high resolution biomedical data conversion applications by employing low complexity two-path all-pass based decimation filters. The performance of the proposed decimation chain was validated by using the MIT-BIH arrhythmia database and comparative simulations were conducted with the state of the art

Bioengineering bacterial outer membrane vesicles as delivery system for RNA therapeutics targeted to lung epithelial cytosols

Intact epithelia lining the airways and alveoli in the lung are essential to maintain lung function. Structural or functional damage of epithelial cells leads in severe diseases, including COPD/emphysema, ibrosis or ALI/ARDS. This central role of epithelia in pulmonary diseases identifies these cells as primary candidates for targeted therapy. With the exception of surface-expressed molecules, however, targeting intracellular components is severely restricted due to poor delivery. We aim to overcome this obstacle using topically administered, bioengineered, biocompatible bacterial outer membrane vesicles (OMVs) as recombinant drug delivery systems for novel biopharmaceuticals. Engineering recombinant surface expression of eukaryotic receptor ligands in ClearColi®, a commercial E.coli BL21 (DE3) strain deficient in lipopolysaccharide production, we have used red fluorescent protein reporters to track OMV loading, transgene expression, and eukaryotic cell trafficking. We demonstrate statistically significant differences in the levels of over 700 proteins between differentially engineered and purified OMV preps with additional differences in transcriptome and lipidome consistency. We also characterised visual and particle size differences observed by transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). Here we report early bioadhesion and culture of re-differentiated lung epithelia. This project aims to bridge the biotechnological gap in the intracellular biopharmaceutics drug delivery challenge for respiratory epithelia through highly controlled, and scalable bio-nanotechnology process. If successful, our work will unlock intracellular imaging and therapeutics research for respiratory diseases with a significant epithelial component, paving the way for other targeting ligands and potentially non-respiratory indications. cellular uptake results in A549 culture as well as air-liquid interface

Germination response of Arabidopsis toconcentration of Nitrates in an Aquaponic Hydroponic system

Arabidopsis thaliana (ecotype Columbia) was planted on sponges in 4 tanks with continuous aeration through airstones. Different amounts of fish under the same growing conditions for 5 weeks. An equal food-fish ratio was given to all tanks except the control which was a hydroponic setup with Murashige and Skoog solution, a plant growth medium, at 1/5 strength. The growth solution was changed once per week and fish water was partially changed every two weeks. pH, temperature, ammonia, nitrite, nitrate and plant number were recorded once per week. A slow growth was observed in all tanks and the control treatment died on the 3rd week

Inflation targeting and inflation convergence: international evidence

We examine whether the inflation rates of the countries that pursue inflation targeting policies have converged as opposed to the experience of the OECD non-inflation targeters. Using a methodology introduced by Pesaran (2007a), we examine the stationarity properties 0f the inflation differentials. This approach has the advantage of avoiding setting arbitrarily a specific country as the benchmark economy. Our results indicate that the inflation rates converge irrespective of the monetary policy framework

A novel method based on Bayesian regularized artificial neural networks for measurement uncertainty evaluation

Coordinate measuring machines (CMMs) are complex measuring systems that are widely used in manufacturing industry for form, size, position, and orientation assessment. In essence, these systems collect a set of individual data points that in practice is often a relatively small sample of an object. Their software then processes these points in order to produce a geometric result or to establish a local coordinate system from datum features. The subject of CMM evaluation is a broad and multifaceted one. This paper is concerned with the uncertainty in the coordinates of each point within the measuring volume of the CMM. Therefore, a novel method for measurement uncertainty evaluation using limited-size data sets is conceived and developed. The proposed method is based on a Bayesian regularized artificial neural network (BRANN) model consisting of three inputs and one output. The inputs are: the nominal coordinates; the ambient temperature; and the temperature of the workpiece. The output is the measured (actual) coordinates. An algorithm is developed and implemented before training the BRANN in order to map each nominal coordinate associated with the other inputs to the target coordinate. For validation the model is trained using a relatively small sample size of ten data sets to predict the variability of a larger sample size of ninety data sets. The calculated uncertainty is improved by more than 80% using the predicted variability compared to the uncertainty from the limited sample data set