Cyclic olefin copolymer (COC) as a promising biomaterial for affecting bacterial colonization: investigation on Vibrio campbellii.

Cyclic olefin copolymer (COC) has emerged as an interesting biocompatible material for Organ-on-a-Chip (OoC) devices monitoring growth, viability, and metabolism of cells. Despite ISO 10993 approval, systematic investigation of bacteria grown onto COC is a still not documented issue. This study discusses biofilm formations of the canonical wild type BB120 Vibrio campbellii strain on a native COC substrate and addresses the impact of the physico-chemical properties of COC compared to conventional hydroxyapatite (HA) and poly(dimethylsiloxane) (PDMS) surfaces. An interdisciplinary approach combining bacterial colony counting, light microscopy imaging and advanced digital image processing remarks interesting results. First, COC can reduce biomass adhesion with respect to common biopolymers, that is suitable for tuning biofilm formations in the biological and medical areas. Second, remarkably different biofilm morphology (dendritic complex patterns only in the case of COC) was observed among the examined substrates. Third, the observed biofilm morphogenesis was related to the interaction of COC with the conditioning layer of the planktonic biological medium. Fourth, Level Co-occurrence Matrix (CGLM)-based analysis enabled quantitative assessment of the biomass textural fractal development under different coverage conditions. All of this is of key practical relevance in searching innovative biocompatible materials for pharmaceutical, implantable and medical products.

Mutant-Dependent Local Orientational Correlation in Biofilms of Vibrio campbellii Revealed through Digital Processing of Light Microscopy Images.

Biofilms are key bacterial communities in genetic and adaptive resistance to antibiotics as well as disease control strategies. The mature high-coverage biofilm formations of the Vibrio campbellii strains (wild type BB120 and isogenic derivatives JAF633, KM387, and JMH603) are studied here through the unstraightforward digital processing of morphologically complex images without segmentation or the unrealistic simplifications used to artificially simulate low-density formations. The main results concern the specific mutant- and coverage-dependent short-range orientational correlation as well as the coherent development of biofilm growth pathways over the subdomains of the image. These findings are demonstrated to be unthinkable based only on a visual inspection of the samples or on methods such as Voronoi tessellation or correlation analyses. The presented approach is general, relies on measured rather than simulated low-density formations, and could be employed in the development of a highly efficient screening method for drugs or innovative materials.

MEED: A novel robust contrast enhancement procedure yielding highly-convergent thresholding of biofilm images.

Morphological and statistical investigation of biofilm images may be even more critical than the image acquisition itself, in particular in the presence of morphologically complex distributions, due to the unavoidable impact of the measurement technique too. Hence, digital image pre-processing is mandatory for reliable feature extraction and enhancement preliminary to segmentation. Also, pattern recognition in automated deep learning (both supervised and unsupervised) models often requires a preliminary effective contrast-enhancement. However, no universal consensus exists on the optimal contrast enhancement approach. This paper presents and discusses a new general, robust, reproducible, accurate and easy to implement contrast enhancement procedure, briefly named MEED-procedure, able to work on images with different bacterial coverages and biofilm structures, coming from different imaging instrumentations (herein stereomicroscope and transmission microscope). It exploits a proper succession of basic morphological operations (erosion and dilation) and a horizontal line structuring element, to minimize the impact on size and shape of the even finer bacterial features. It systematically enhances the objects of interest, without histogram stretching and/or undesirable artifacts yielded by common automated methods. The quality of the MEED-procedure is ascertained by segmentation tests which demonstrate its robustness regarding the determination of threshold and convergence of the thresholding algorithm. Extensive validation tests over a rich image database, comparison with the literature and comprehensive discussion of the conceptual background support the superiority of the MEED-procedure over the existing methods and demonstrate it is not a routine application of morphological operators.

Aptamers Which Target Proteins: What Proteotronics Suggests to Pharmaceutics.

Aptamers represent a challenging field of research, relevant for diagnosis in macular degeneration, cancer, thrombosis and many inflammatory diseases, and promising in drug discovery and development. Their selection is currently performed by a stable in vitro technology, namely, SELEX. Furthermore, computationalstatistical tools have been developed to complement the SELEX selection; they work both in the preliminary stage of selection, by designing high affinity aptamers for the assigned target, and also in the final stage, analyzing the features of the best performers to implement the selection technique further. A massive use of the in silico approach is, at present, only restricted by the limited knowledge of the specific aptamer-target topology. Actually, only about fifty X-ray structures of aptamer-protein complexes have been experimentally resolved, highlighting how this knowledge has to be improved. The structure of biomolecules like aptamer-protein complexes can be represented by networks, from which several parameters can be extracted. This work briefly reviews the literature, discussing if and how general network parameters in the framework of Proteotronics and graph theory (such as electrical features, link number, free energy change, and assortativity), are important in characterizing the complexes, anticipating some features of the biomolecules. To better explain this topic, a case-study is proposed, constituted by a set of anti-angiopoietin (Ang2) aptamers, whose performances are known from the experiments, and for which two different types of conformers were predicted. A topological indicator is proposed, named Möbius (M), which combines local and global information, and seems able to discriminate between the two possible types of conformers, so that it can be considered as a useful complement to the in vitro screening for pharmaceutical aims.

Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors.

Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.

Hierarchy and Assortativity as New Tools for Binding-Affinity Investigation: The Case of the TBA Aptamer-Ligand Complex.

Aptamers are single stranded DNA, RNA, or peptide sequences having the ability to bind several specific targets (proteins, molecules as well as ions). Therefore, aptamer production and selection for therapeutic and diagnostic applications is very challenging. Usually, they are generated in vitro, although computational approaches have been recently developed for the in silico production. Despite these efforts, the mechanism of aptamer-ligand formation is not completely clear, and producing high-affinity aptamers is still quite difficult. This paper aims to develop a computational model able to describe aptamer-ligand affinity. Topological tools, such as the conventional degree distribution, the rank-degree distribution (hierarchy), and the node assortativity are employed. In doing so, the macromolecules tertiary-structures are mapped into appropriate graphs. These graphs reproduce the main topological features of the macromolecules, by preserving the distances between amino acids (nucleotides). Calculations are applied to the thrombin binding aptamer (TBA), and the TBA-thrombin complex produced in the presence of Na+ or K+. The topological analysis is able to detect several differences between complexes obtained in the presence of the two cations, as expected by previous investigations. These results support graph analysis as a novel computational tool for testing affinity. Otherwise, starting from the graphs, an electrical network can be obtained by using the specific electrical properties of amino acids and nucleobases. Therefore, a further analysis concerns with the electrical response, revealing that the resistance is sensitively affected by the presence of sodium or potassium, thus suggesting resistance as a useful physical parameter for testing binding affinity.

Robust Intensity Standardization in Brain Magnetic Resonance Images.

The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

Automatic lung segmentation in CT images with accurate handling of the hilar region.

A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent 'fusion' between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the framework of a CAD system. Finally, in the comparison with a two-dimensional segmentation procedure, inter-slice smoothness was calculated, showing that the masks created by the 3D algorithm are significantly smoother than those calculated by the 2D-only procedure.

A novel multithreshold method for nodule detection in lung CT.

Multislice computed tomography (MSCT) is a valuable tool for lung cancer detection, thanks to its ability to identify noncalcified nodules of small size (from about 3 mm). Due to the large number of images generated by MSCT, there is much interest in developing computer-aided detection (CAD) systems that could assist radiologists in the lung nodule detection task. A complete multistage CAD system, including lung boundary segmentation, regions of interest (ROIs) selection, feature extraction, and false positive reduction is presented. The selection of ROIs is based on a multithreshold surface-triangulation approach. Surface triangulation is performed at different threshold values, varying from a minimum to a maximum value in a wide range. At a given threshold value, a ROI is defined as the volume inside a connected component of the triangulated isosurface. The evolution of a ROI as a function of the threshold can be represented by a treelike structure. A multithreshold ROI is defined as a path on this tree, which starts from a terminal ROI and ends on the root ROI. For each ROI, the volume, surface area, roundness, density, and moments of inertia are computed as functions of the threshold and used as input to a classification system based on artificial neural networks. The method is suitable to detect different types of nodules, including juxta-pleural nodules and nodules connected to blood vessels. A training set of 109 low-dose MSCT scans made available by the Pisa center of the Italung-CT trial and annotated by expert radiologists was used for the algorithm design and optimization. The system performance was tested on an independent set of 23 low-dose MSCT scans coming from the Pisa Italung-CT center and on 83 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. On the Italung-CT test set, for nodules having a diameter greater than or equal to 3 mm, the system achieved 84% and 71% sensitivity at false positive/scan rates of 10 and 4, respectively. For nodules having a diameter greater than or equal to 4 mm, the sensitivities were 97% and 80% at false positive/scan rates of 10 and 4, respectively. On the LIDC data set, the system achieved a 79% sensitivity at a false positive/scan rate of 4 in the detection of nodules with a diameter greater than or equal to 3 mm that have been annotated by all four radiologists.