Plasma-potentiated small molecules—possible alternative to antibiotics?

Figure 5.

Kateryna Bazaka1,2,3,4, Olha Bazaka3, Igor Levchenko1,5, Shuyan Xu5, Elena P Ivanova6, Michael Keidar7 and Kostya (Ken) Ostrikov1,4

1 School of Chemistry, Physics, Mechanical Engineering, Queensland University of Technology, Brisbane, Queensland 4000, Australia: 2 Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland 4000, Australia: 3 College of Science and Engineering, James Cook University, Townsville, QLD 4814, Australia; 4 CSIRO-QUT Joint Sustainable Processes and Devices Laboratory, Commonwealth Scientific and Industrial Research Organization, PO Box 218, Lindfield, NSW 2070, Australia; 5 Plasma Sources and Applications Centre, NIE, Nanyang Technological University, Singapore; 6 Department of Chemistry and Biotechnology, School of Science, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; 7 School of Medicine and Health Sciences, The George Washington University, Washington DC 20052, United States of America.

DOI: 10.1088/2399-1984/aa80d3

The efficacy of the existing arsenal of antibiotics is continuously compromised by their indiscriminative and often excessive use. The antibiotic arsenal can be expanded with agents that have different mechanisms of activity to conventional drugs, such as plant-derived natural antimicrobial small molecules, yet these often lack sufficient activity and selectivity to fulfill the antibiotics requirements and conventional thermochemical methods of their transient activation may not be compatible with biomedical applications. Here, non-equilibrium conditions of atmospheric-pressure plasma are used for rapid, single-step potentiation of activity of select terpenes without the use of chemicals or heating. Substantial potentiation of activity against Staphylococcus aureus cells in planktonic and biofilm states is observed in both inherently antibacterial terpenes, e.g. terpinen-4-ol, and compounds generally considered to have limited effect against S. aureus, e.g. γ-terpinene. The improved biological activity may arise, at least in part, from the changes in the physico–chemical properties of the terpenes induced by plasma-generated chemical species and physical effects, such as electric fields and UV irradiation. This activation approach is generic, and thus can potentially be applied to other molecules and their mixtures in an effort to expand the range of effective antimicrobial agents for deactivation of pathogenic organisms in hygiene, medical and food applications.

Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change

Fig. 1

Owen A. Williams,a, Eva A. Zeestraten,a, Philip Benjamin,b, Christian Lambert,a, Andrew J. Lawrence,c, Andrew D. Mackinnon,d, Robin G. Morris,e, Hugh S. Markus,c, Rebecca A. Charlton,f, Thomas R. Barrick,a

a Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George’s University of London, London, UK; b Department of Radiology, Charing Cross Hospital Campus, Imperial College NHS Trust, London, UK; c Stroke Research Group, Clinical Neurosciences, University of Cambridge, Cambridge, UK; d Atkinson Morley Regional Neuroscience Centre, St George’s NHS Healthcare Trust, London, UK; e Department of Psychology, King’s College Institute of Psychiatry, Psychology, and Neuroscience, London, UK; f Department of Psychology, Goldsmiths University of London, London, UK.

DOI: 10.1016/j.nicl.2017.08.016

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period.

98 patients (aged 43–89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients’ brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials.

Changes in brain structure described by DSEG θ were related to change in EF and IPS (p < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p = 0.002). Change in DSEG θ was also related to change in all other MRI markers (p < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity.

DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs).

Virtual cardiac monolayers for electrical wave propagation

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Nina Kudryashova,1,2 Valeriya Tsvelaya,2 Konstantin Agladze,2, Alexander Panfilov1
1Department of Physics and Astronomy, Gent University, Gent, 9000 Belgium; 2Laboratory of Biophysics of Excitable Systems, Moscow Institute of Physics and Technology, Dolgoprudny, 141701 Moscow Region Russia.
The complex structure of cardiac tissue is considered to be one of the main determinants of an arrhythmogenic substrate. This study is aimed at developing the first mathematical model to describe the formation of cardiac tissue, using a joint in silicoin vitro approach. First, we performed experiments under various conditions to carefully characterise the morphology of cardiac tissue in a culture of neonatal rat ventricular cells. We considered two cell types, namely, cardiomyocytes and fibroblasts. Next, we proposed a mathematical model, based on the Glazier-Graner-Hogeweg model, which is widely used in tissue growth studies. The resultant tissue morphology was coupled to the detailed electrophysiological Korhonen-Majumder model for neonatal rat ventricular cardiomyocytes, in order to study wave propagation. The simulated waves had the same anisotropy ratio and wavefront complexity as those in the experiment. Thus, we conclude that our approach allows us to reproduce the morphological and physiological properties of cardiac tissue.

Spiral Form of the Human Cochlea Results from Spatial Constraints

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M. Pietsch,1 L. Aguirre Dávila,2 P. Erfurt,1 E. Avci,1 T. Lenarz, A. Kral3
1Institute of AudioNeuroTechnology & Dept. of Experimental Otology, ENT Clinics, School of Medicine, Hanover Medical University, Hanover, Germany; 2Institute of Biometry, School of Medicine, Hanover Medical University, Hanover, Germany; 3School of Behavioral and Brain Sciences, The University of Texas, Dallas, USA.
The human inner ear has an intricate spiral shape often compared to shells of mollusks, particularly to the nautilus shell. It has inspired many functional hearing theories. The reasons for this complex geometry remain unresolved. We digitized 138 human cochleae at microscopic resolution and observed an astonishing interindividual variability in the shape. A 3D analytical cochlear model was developed that fits the analyzed data with high precision. The cochlear geometry neither matched a proposed function, namely sound focusing similar to a whispering gallery, nor did it have the form of a nautilus. Instead, the innate cochlear blueprint and its actual ontogenetic variants were determined by spatial constraints and resulted from an efficient packing of the cochlear duct within the petrous bone. The analytical model predicts well the individual 3D cochlear geometry from few clinical measures and represents a clinical tool for an individualized approach to neurosensory restoration with cochlear implants.

BRAPH: A graph theory software for the analysis of brain connectivity

Mite Mijalkov,1, Ehsan Kakaei,1, Joana B. Pereira,2, Eric Westman,2, Giovanni Volpe,3,4

1 UNAM—National Nanotechnology Research Center & Department of Physics, Bilkent University, Ankara, Turkey; 2  Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; 3 UNAM – National Nanotechnology Research Center & Department of Physics, Bilkent University, Ankara, Turkey; 4  Department of Physics, Goteborg University, Goteborg, Sweden.

DOI: 10.1371/journal.pone.0178798

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

Biomaterial-Free Three-Dimensional Bioprinting of Cardiac Tissue using Human Induced Pluripotent Stem Cell Derived Cardiomyocytes

Figure 2

Chin Siang Ong,1,2, Takuma Fukunishi,1, Huaitao Zhang,1, Chen Yu Huang,2, Andrew Nashed,2, Adriana Blazeski,3, Deborah DiSilvestre,2, Luca Vricella,1, John Conte,1, Leslie Tung,3, Gordon F. Tomaselli,2, Narutoshi Hibino,1

1 Division of Cardiac Surgery, Johns Hopkins Hospital, Baltimore, Maryland, USA; 2 Division of Cardiology, Johns Hopkins Hospital, Baltimore, Maryland, USA; 3 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

DOI: 10.1038/s41598-017-05018-4

We have developed a novel method to deliver stem cells using 3D bioprinted cardiac patches, free of biomaterials. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), fibroblasts (FB) and endothelial cells (EC) were aggregated to create mixed cell spheroids. Cardiac patches were created from spheroids (CM:FB:EC = 70:15:15, 70:0:30, 45:40:15) using a 3D bioprinter. Cardiac patches were analyzed with light and video microscopy, immunohistochemistry, immunofluorescence, cell viability assays and optical electrical mapping. Cardiac tissue patches of all cell ratios beat spontaneously after 3D bioprinting. Patches exhibited ventricular-like action potential waveforms and uniform electrical conduction throughout the patch. Conduction velocities were higher and action potential durations were significantly longer in patches containing a lower percentage of FBs. Immunohistochemistry revealed staining for CM, FB and EC markers, with rudimentary CD31+ blood vessel formation. Immunofluorescence revealed the presence of Cx43, the main cardiac gap junction protein, localized to cell-cell borders. In vivo implantation suggests vascularization of 3D bioprinted cardiac patches with engraftment into native rat myocardium. This constitutes a significant step towards a new generation of stem cell-based treatment for heart failure.


Shifting the optimal stiffness for cell migration

Figure 2

Benjamin L. Bangasser,1, Ghaidan A. Shamsan,1, Clarence E. Chan,1, Kwaku N. Opoku,1, Erkan Tüzel,1, Benjamin W. Schlichtmann,1, Jesse A. Kasim,1, Benjamin J. Fuller,1, Brannon R. McCullough,1, Steven S. Rosenfeld,2, David J. Odde,1

1 Department of Biomedical Engineering, University of Minnesota, 312 Church Street SE, Minneapolis, Minnesota 55455, USA; 2 Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA.

DOI: 10.1038/ncomms15313

Cell migration, which is central to many biological processes including wound healing and cancer progression, is sensitive to environmental stiffness, and many cell types exhibit a stiffness optimum, at which migration is maximal. Here we present a cell migration simulator that predicts a stiffness optimum that can be shifted by altering the number of active molecular motors and clutches. This prediction is verified experimentally by comparing cell traction and F-actin retrograde flow for two cell types with differing amounts of active motors and clutches: embryonic chick forebrain neurons (ECFNs; optimum 1 kPa) and U251 glioma cells (optimum 100 kPa). In addition, the model predicts, and experiments confirm, that the stiffness optimum of U251 glioma cell migration, morphology and F-actin retrograde flow rate can be shifted to lower stiffness by simultaneous drug inhibition of myosin II motors and integrin-mediated adhesions.


Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease


Maxwell B. Wang,1, Julia P. Owen,1, Pratik Mukherjee,1,2, Ashish Raj,3

1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, United States of America; 2 Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, California, United States of America; 3 Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America.

DOI: 10.1371/journal.pcbi.1005550

Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.

High-Throughput Quantification of Nanoparticle Degradation Using Computational Microscopy and Its Application to Drug Delivery Nanocapsules


Aniruddha Ray†‡, Shuoran Li§, Tatiana Segura‡§∥⊥, Aydogan Ozcan*†‡⊥# 
Electrical Engineering Department; Bioengineering Department; §Department of Chemical and Biomolecular Engineering; Department of Medicine, Dermatology; #Department of Surgery, David Geffen School of Medicine; California NanoSystems  Institute (CNSI), University of California, Los Angeles, California 90095, United States
Design and synthesis of degradable nanoparticles are very important in drug delivery and biosensing fields. Although accurate assessment of nanoparticle degradation rate would improve the characterization and optimization of drug delivery vehicles, current methods rely on estimating the size of the particles at discrete points over time using, for example, electron microscopy or dynamic light scattering (DLS), among other techniques, all of which have drawbacks and practical limitations. There is a significant need for a high-throughput and cost-effective technology to accurately monitor nanoparticle degradation as a function of time and using small amounts of sample. To address this need, here we present two different computational imaging-based methods for monitoring and quantification of nanoparticle degradation. The first method is suitable for discrete testing, where a computational holographic microscope is designed to track the size changes of protease-sensitive protein-core nanoparticles following degradation, by periodically sampling a subset of particles mixed with proteases. In the second method, a sandwich structure was utilized to observe, in real-time, the change in the properties of liquid nanolenses that were self-assembled around degrading nanoparticles, permitting continuous monitoring and quantification of the degradation process. These cost-effective holographic imaging based techniques enable high-throughput monitoring of the degradation of any type of nanoparticle, using an extremely small amount of sample volume that is at least 3 orders of magnitude smaller than what is required by, for example, DLS-based techniques.

Quantitative assessment of Cerenkov luminescence for radioguided brain tumor resection surgery

Figure 7.

Justin S Klein, Gregory S Mitchell, Simon R Cherry

Department of Biomedical Engineering, University of California, Davis, CA, United States of America

DOI: 10.1088/1361-6560/aa6641

Cerenkov luminescence imaging (CLI) is a developing imaging modality that detects radiolabeled molecules via visible light emitted during the radioactive decay process. We used a Monte Carlo based computer simulation to quantitatively investigate CLI compared to direct detection of the ionizing radiation itself as an intraoperative imaging tool for assessment of brain tumor margins. Our brain tumor model consisted of a 1 mm spherical tumor remnant embedded up to 5 mm in depth below the surface of normal brain tissue. Tumor to background contrast ranging from 2:1 to 10:1 were considered. We quantified all decay signals (e±, gamma photon, Cerenkov photons) reaching the brain volume surface. CLI proved to be the most sensitive method for detecting the tumor volume in both imaging and non-imaging strategies as assessed by contrast-to-noise ratio and by receiver operating characteristic output of a channelized Hotelling observer.