inkjet sensors produced by consumer printers with smartphone impedance readout

Abstract Inkjet printing technology is showing a disruptive potential for low-cost optical and electrochemical biosensors fabrication. This technology is becoming affordable for every laboratory, potentially allowing every research group to implement a biosensors fabrication platform with consumer inkjet printers, commercially available inks and smartphones for readout. In the present work we developed an example of such platform testing several inks, printers, and substrates. We defined and optimized the protocols assessing the printing limits and the fabricated biosensors electrochemical properties in standard solutions. Our platform has a total cost of less than 450 Euro and a single sensor fabrication cost of 0.026 Euro. Finally, we tested the sensitivity of smartphone-performed impedance measurements with printed biosensors surface coverage by Self Assembling Monolayers (SAM), validating them with standard instruments

Effect of Size and Heterogeneity of Samples on Biomarker Discovery: Synthetic and Real Data Assessment

MOTIVATION: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods. METHODS: We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state. RESULTS: The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results

A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA

This paper presents the Probabilistic Risk Assessment (PRA) developed for crucial components of the SPES (Selective Production of Exotic Species) nuclear facility, a research plant currently under construction at INFN’s Legnaro National Laboratories. The study systematically analyzes the most serious failure scenarios of the SPES Front-End and the remote handling systems, with the aim of identifying critical issues and improving the systems in terms of operational safety, hardware design, robustness and reliability. The approach adopted in this study is a blended methodology based on semi-quantitative techniques. HAZOP (HAZard and OPerability analysis) and LOPA (Layer Of Protection Analysis) have been applied to evaluate the risk of failure scenarios during operation and to assess their consequences. This study identified some weaknesses in the system’s design, leading to possible undesirable behaviors. A number of safeguards and recommendations have been proposed, as well as a selection of design improvements related to the robustness and maintainability of pivotal components. The results of this assessment support the validity of the proposed Independent Protection Layers (IPLs) adopted to ensure operational safety and optimize maintenance interventions in such highly radioactive environment

Evaluation of feature stability on simulated data.

<p>Boxplots of the core Canberra distance between lists of selected features obtained using different methods when 50, 20, 15 or 10 subjects per group are available. A star highlights the significant differences between pair of bootstrap and non-bootstrap approaches (p-value lower than 0.05, Wilcoxon test).</p

Evaluation of feature ranking on simulated data.

<p>Boxplots of area under the precision <i>vs.</i> recall curves obtained by ranking features according to the different methods, when 50, 20, 15 or 10 subjects per group are available.</p

MCC corresponding to the optimal number of features obtained using different methods – real data.

<p>Average MCC obtained when 20 subjects per group are available, sampled from datasets GSE2990 and GSE7390 MCC (range of values is indicated in parenthesis), and obtained on the complete datasets GSE2990 and GSE7390.</p

Variability of the diseased population.

<p>Histogram of the Euclidean distance between the steady states of the diseased population with respect to the original phenotype.</p

Evaluation of feature stability on real data.

<p>Boxplots of the core Canberra distance between lists of selected features provided by different classification methods when 20 subjects per group are repeatedly sampled from GSE2990 (upper panel) and GSE7390 (middle panel) datasets. A star highlights the significant differences between pair of bootstrap and non-bootstrap approaches (p-value lower than 0.05, Wilcoxon test). The interquartile range of the number of selected features is reported below each boxplot. The core Canberra distances between lists of biomarkers provided by different methods on the complete GSE2990 <i>vs.</i> GSE7390 datasets are shown in the lower panel together with the number of selected features in each dataset.</p

Precision of feature selection on simulated data.

<p>Boxplots of precision corresponding to the optimal number of features chosen by different methods when 50, 20, 15 or 10 subjects per group are available. A star highlights the significant differences between pair of bootstrap and non-bootstrap approaches (p-value lower than 0.05, Wilcoxon test). The interquartile range of the number of selected features is also reported below each boxplot.</p