Microfluidic analysis techniques for safety assessment of pharmaceutical nano- and microsystems

This chapter reviews the evolution of microfabrication methods and materials, applicable to manufacturing of micro total analysis systems (or lab‐on‐a‐chip), from a general perspective. It discusses the possibilities and limitations associated with microfluidic cell culturing, or so called organ‐on‐a‐chip technology, together with selected examples of their exploitation to characterization of pharmaceutical nano‐ and microsystems. Materials selection plays a pivotal role in terms of ensuring the cell adhesion and viability as well as defining the prevailing culture conditions inside the microfluidic channels. The chapter focuses on the hepatic safety assessment of nanoparticles and gives an overview of the development of microfluidic immobilized enzyme reactors that could facilitate examination of the hepatic effects of nanomedicines under physiologically relevant conditions. It also provides an overview of the future prospects regarding system‐level integration possibilities facilitated by microfabrication of miniaturized separation and sample preparation systems as integral parts of microfluidic in vitro models.Non peer reviewe

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

Video segmentation using fast marching and region growing algorithms,” presented at the Workshop Image Analysis for Multimedia Interactive Services

The algorithm presented in this paper is comprised of three main stages: (1) classi cation of the image sequence and, in tha case of a moving camera, parametric motion estimation, (2) change detection having as reference a xed frame, an appropriately selected frame or a displaced frame, and (3) object localisation using local colour features. The image sequence classi cation is based on statistical tests on the frame di erence. The change detection module uses a two-label fast marching algorithm. Finally, the object localisation uses a region growing algorithm based on the colour similarity. Video object segmentation results are shown using the COST 211 data set

Analysis of quantitative profiles of GI education: towards an analytical basis for EduMapping

Agent-oriented models are used in organization and information system modelling for providing intentional descriptions of processes as a network of relationships among actors. As such, they capture and represent goals, dependencies, intentions, beliefs, alternatives, etc., which appear in several contexts: business process reengineering, information system development, etc. In this paper, we are interested in the definition of a framework for the analysis of the properties that these models exhibit. Indicators and metrics for these properties are defined in terms of the model elements (e.g., actors, dependencies, scenario paths, etc.) Our approach is basically quantitative in nature, which allows defining indicators and metrics that can be reused in many contexts. However, a qualitative component can be introduced if trustable expert knowledge is available; the extent up to which quantitative and qualitative aspects are intertwined can be determined in every single case. We apply our proposal to the i* notation and we take as main case study a highly-intentional property, predictability of model element

Protein cross-linking capillary electrophoresis at increased throughput for a range of protein–protein interactions

Tools for measuring affinities and stoichiometries of protein-protein complexes are valuable for elucidating the role of protein-protein interactions (PPIs) in governing cell functions and screening for PPI modulators. Such measurements can be challenging because PPIs can span a wide range of affinities and include stoichiometries from dimers to high order oligomers. Also, most techniques require large amounts of protein which can hamper research for difficult to obtain proteins. Protein cross-linking capillary electrophoresis (PXCE) has the potential to directly measure PPIs and even resolve multiple PPIs while consuming attomole quantities. Previously PXCE has only been used for high affinity, 1 : 1 complexes; here we expand the utility of PXCE to access a wide range of PPIs including weak and multimeric oligomers. Use of glutaraldehyde as the cross-linking agent was key to advancing the method because of its rapid reaction kinetics. A 10 s reaction time was found to be sufficient for cross-linking and quantification of seven different PPIs with Kd values ranging from low μM to low nM including heat shock protein 70 (Hsp70) interacting with heat shock organizing protein (3.8 ± 0.7 μM) and bcl2 associated anthanogene (26 ± 6 nM). Non-specific cross-linking of protein aggregates was found to be minimal at protein concentrations <20 μM as assessed by size exclusion chromatography. PXCE was sensitive enough to measure changes in PPI affinity induced by the protein nucleotide state or point mutations in the protein-binding site. Further, several interactions could be resolved in a single run, including Hsp70 monomer, homodimer and Hsp70 complexed the with c-terminus of Hsp70 interacting protein (CHIP). Finally, the throughput of PXCE was increased to 1 min per sample suggesting potential for utility in screening

Morphology and Separation Efficiency of Low-Aspect-Ratio Capillary Ultrahigh Pressure Liquid Chromatography Columns

We derive a quantitative relationship between the bed morphology and the chromatographic separation efficiency of capillary columns packed with sub-2 μm particles, covering capillary inner diameters from 10 to 75 μm. Our study focuses on wall effects and their impact on band broadening at increasing column-to-particle diameter (aspect) ratios. We approach these complex effects by a morphological analysis of reconstructed column segments composed of several thousand particles that were imaged by confocal laser scanning microscopy. Radial interparticle porosity profiles including wall effects are quantified through an integral porosity deviation, a scalar measure that proves to be a general descriptor of transcolumn porosity heterogeneity. It correlates with the associated transcolumn eddy dispersion, which dominates band broadening in the capillaries and is visualized in the plate height curves by a simple velocity-proportional term. Our comprehensive approach identifies the packing structure features that contribute to decreased efficiency as reflected, e.g., in subtle variations of the wall effect at different aspect ratios, or a particle size-segregation effect in larger-diameter columns as a result of an increased number of packing voids near the wall–bed interface