Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

Here, using an integrative experimental and computational approach, Imle et al. show how cell motility and density affect HIV cell-associated transmission in a three-dimensional tissue-like culture system of CD4+ T cells and collagen, and how different collagen matrices restrict infection by cell-free virions

Digest of Russian Space Life Sciences, issue 33

This is the thirty-third issue of NASA's USSR Space Life Sciences Digest. It contains abstracts of 55 papers published in Russian journals. The abstracts in this issue have been identified as relevant to the following areas of space biology and medicine: biological rhythms, body fluids, botany, cardiovascular and respiratory systems, developmental biology, endocrinology, equipment and instrumentation, gastrointestinal system, genetics, hematology, human performance, metabolism, microbiology, musculoskeletal system, neurophysiology, nutrition, operational medicine, psychology, radiobiology, and reproductive system

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Novel Techniques For Investigating The Regulation Of Skeletal Muscle Hemodynamics

The effect of the sympathetic nervous system (SNS) on blood flow distribution within skeletal muscle microvasculature is conditional upon regional activation of SNS receptors. Due to a lack of appropriate experimental models and techniques, no study has systematically evaluated the effect of SNS receptor activation in continuously branching skeletal muscle arteriolar trees. In line with previous work, we hypothesize that there will be a spatially-dependent distribution of sympathetic receptor activation along the arteriolar tree. Specifically, we anticipate a progressive decrease of adrenergic activation and a progressive increase of peptidergic and purinergic activation with increasing arteriolar order. We developed a novel rat gluteus maximus (GM) muscle preparation which provided access to a large vascular network, from which we developed an experimental method for collecting cell velocity profiles in fast-flowing arterioles. Using these data, we derived an empirical relationship between velocity ratio (VMax/VMean) and arteriolar diameter, collected novel data on cell free layer width and estimated wall shear rates, and derived a wall shear rate equation from experimental data that can be used for calculating wall shear rates in skeletal muscle microvasculature. We evaluated SNS receptor activation (α1R, α2R, NPY1R, and P2X1R) in continuously branching arteriolar trees in the rat GM, as a function of network topology. A computational flow model estimated the total flow, resistance, and red blood cell flow heterogeneity. For the first time, we highlight effects of SNS receptor activation on network hemodynamics, where proximal arterioles responded most to adrenergic activation, while distal arterioles responded most to Y1R and P2X1R activation. Our data highlight the functional consequences of topologically-dependent SNS receptor activation. The tools developed in this thesis are beneficial for computing hemodynamic parameters from in vivo data, as well as providing input variables to and validation of computational flow models

Modeling Oxygen Transport in Three-Dimensional Capillary Networks

The purpose of this thesis was to examine how the use of real 3-dimensional (3D) capillary network geometries affect models of oxygen transport to tissue. Software was developed to reconstruct microvascular geometry in 3D from intravital video. Characterization of 3D reconstructions demonstrated that capillary density, length and capillary diameter were consistent with previous findings. Using reconstructed capillary networks a strategy was devised that utilized red blood cell (RBC) supply rate (SR) as a metric for flow modeling. Applying the RBC SR based flow model on baseline and perturbed flow conditions demonstrated that RBC SR is a major determinant of oxygen delivery that is insensitive to changes in flow distribution. The resulting flow solutions were used for comparing oxygen transport in 3D networks and synthetic parallel arrays. A variety of physiological conditions were simulated and it was determined that parallel arrays resulted in oxygen transport solutions with higher mean PO2 due to homogeneous distribution of vessels in the volume. Lastly, to investigate oxygen transport in a complex pathology a model of sepsis was used to investigate how incremental perfusion loss, consumption increase and change in RBC SR affect oxygen delivery. It was shown that perfusion loss did not markedly impair oxygen delivery provided that RBC SR was maintained. These results have improved our understanding of oxygen transport to tissue in normal and diseased conditions; the use of reconstructed networks and measurements of blood flow & oxygen saturation in computer models provides different solutions than those using statistical averages and synthetic networks

Intelligent computational system for colony-forming-unit enumeration and differentiation

Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in a wide range of fields. The enumeration processes of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular pathologies require an urgent and accurate response for the therapy to be effective. To reduce time limitations and perhaps discrepancies, this work introduces an intelligent image processing software capable of automatically quantifying the number of Colony Forming Units (CFUs) in Petri-plates. This rapid enumeration enables the technician to provide an expeditious assessment of the microbial load. Moreover, an auxiliary system is able to differentiate among colony images of Echerichia coli, Pseudomonas aeruginosa and Staphylococcus aureus via Machine Learning, based on a Convolutional Neural Network in a process of cross-validation. For testing and validation of the system, the three bacterial groups were cultured, and a significant labeled database was created, exercising suited microbiological laboratory methodologies and subsequent image acquisition. The system demonstrated acceptable accuracy measures; the mean values of precision, recall and F-measure were 95%, 95% and 0.95, for E. coli, 91%, 91% and 0.90 for P. aeruginosa, and 84%, 86% and 0.85 for S. aureus. The adopted deep learning approach accomplished satisfactory results, manifesting 90.31% of accuracy. Ultimately, evidence related to the time-saving potential of the system was achieved; the time spent on the quantification of plates with a high number of colonies might be reduced to a half and occasionally to a third.A análise quantitativa de microrganismos é uma ferramenta essencial na aferição da segurança e qualidade numa ampla variedade de áreas. O processo de enumeração de microrganismos viáveis através das técnicas de cultura tradicionais é económica e metodologicamente adequado, conferindo lhe alta aplicabilidade a nível mundial. Contudo, a contagem manual pode ser morosa, laboriosa e imprecisa. Em adição, certas patologias requerem uma urgente e precisa resposta de modo a que a terapia seja eficaz. De forma a reduzir limitações e discrepâncias, este trabalho apresenta um software inteligente de processamento de imagem capaz de quantificar automaticamente o número de Unidades Formadoras de Colónias (UFCs) em placas de Petri. Esta rápida enumeração, possibilita ao técnico uma expedita avaliação da carga microbiana. Adicionalmente, um sistema auxiliar tem a capacidade de diferenciar imagens de colónias de Echerichia coli, Pseudomonas aeruginosa e Staphylococcus aureus recorrendo a Machine Learning, através de uma Rede Neuronal Convolucional num processo de validação cruzada. Para testar e validar o sistema, os três grupos bacterianos foram cultivados e uma significativa base de dados foi criada, recorrendo às adequadas metodologias microbiológicas laboratoriais e subsequente aquisição de imagens. O sistema demonstrou medidas de precisão aceitáveis; os valores médios de precisão, recall e F-measure, foram 95%, 95% e 0.95, para E. coli, 91%, 91% e 0.90 para P. aeruginosa, e 84%, 86% e 0.85 para S. aureus. A abordagem deep learning obteve resultados satisfatórios, manifestando 90.31% de precisão. O sistema revelou potencial em economizar tempo; a duração de tarefas afetas à quantificação de placas com elevado número de colónias poderá ser reduzida para metade e ocasionalmente para um terço

A practical review on the measurement tools for cellular adhesion force

Cell cell and cell matrix adhesions are fundamental in all multicellular organisms. They play a key role in cellular growth, differentiation, pattern formation and migration. Cell-cell adhesion is substantial in the immune response, pathogen host interactions, and tumor development. The success of tissue engineering and stem cell implantations strongly depends on the fine control of live cell adhesion on the surface of natural or biomimetic scaffolds. Therefore, the quantitative and precise measurement of the adhesion strength of living cells is critical, not only in basic research but in modern technologies, too. Several techniques have been developed or are under development to quantify cell adhesion. All of them have their pros and cons, which has to be carefully considered before the experiments and interpretation of the recorded data. Current review provides a guide to choose the appropriate technique to answer a specific biological question or to complete a biomedical test by measuring cell adhesion

Comprehensive Topological, Geometric, And Hemodynamic Analysis Of The Rat Gluteus Maximus Arteriolar Networks

The objective of this thesis was to measure the geometric and topological properties of complete arteriolar networks within skeletal muscle, and to use these data as ideal inputs in a computational blood flow model in order to analyze the corresponding hemodynamic properties. Specifically, we sought to measure the levels of structural and hemodynamic heterogeneity exhibited within and between arteriolar networks. Intravital videomicroscopy was used to image and construct photomontages of complete arteriolar networks of the rat gluteus maximus muscle under baseline conditions. Arteriolar diameters, lengths, and inter-connections were analyzed and grouped according to a centrifugal ordering scheme. For all networks considered, high levels of inter-network homology were observed with regards to the structure (geometry, topology, fractal dimension) as well as the hemodynamic (blood flow, hematocrit distribution) properties. Blood flow was proportional to the diameter cubed in support of Murray’s Law. Future studies will aim to incorporate capillary and venule data in order to construct a complete model of the microcirculation within the rat gluteus maximus muscle