Search results for: Gabrielle Holt

Authors: Thauane Silva, Gabrielle do Vale, Andre Ferreira, Marilda Siqueira, Thiago Moreno L. Souza, Milene D. Miranda

Abstract:

The exposure of A(H1N1)pdm09-infected epithelial cells (HeLa) to HIV-1 viral particles, or its gp120, enhanced interferon-induced transmembrane protein (IFITM3) content, a viral restriction factor (RF), resulting in a decrease in influenza replication. The gp120 binds to CCR5 (R5) or CXCR4 (X4) cell receptors during HIV-1 infection. Then, it is possible that the endogenous ligands of these receptors also modulate the expression of IFITM3 and other cellular factors that restrict influenza virus replication. Thus, the aim of this study is to analyze the role of cellular receptors R5 and X4 in modulating RFs in order to inhibit the replication of the influenza virus. A549 cells were treated with 2x effective dose (ED50) of endogenous R5 or X4 receptor agonists, CCL3 (20 ng/ml), CCL4 (10 ng/ml), CCL5 (10 ng/ml) and CXCL12 (100 ng/mL) or exogenous agonists, gp120 Bal-R5, gp120 IIIB-X4 and its mutants (5 µg/mL). The interferon α (10 ng/mL) and oseltamivir (60 nM) were used as a control. After 24 h post agonists exposure, the cells were infected with virus influenza A(H3N2) at 2 MOI (multiplicity of infection) for 1 h. Then, 24 h post infection, the supernatant was harvested and, the viral titre was evaluated by qRT-PCR. To evaluate IFITM3 and SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1) protein levels, A549 were exposed to agonists for 24 h, and the monolayer was lysed with Laemmli buffer for western blot (WB) assay or fixed for indirect immunofluorescence (IFI) assay. In addition to this, we analyzed other RFs modulation in A549, after 24 h post agonists exposure by customized RT² Profiler Polymerase Chain Reaction Array. We also performed a functional assay in which SAMHD1-knocked-down, by single-stranded RNA (siRNA), A549 cells were infected with A(H3N2). In addition, the cells were treated with guanosine to assess the regulatory role of dNTPs by SAMHD1. We found that R5 and X4 agonists inhibited influenza replication in 54 ± 9%. We observed a four-fold increase in SAMHD1 transcripts by RFs mRNA quantification panel. After 24 h post agonists exposure, we did not observe an increase in IFITM3 protein levels through WB or IFI assays, but we observed an upregulation up to three-fold in the protein content of SAMHD1, in A549 exposed to agonists. Besides this, influenza replication enhanced in 20% in cell cultures that SAMDH1 was knockdown. Guanosine treatment in cells exposed to R5 ligands further inhibited influenza virus replication, suggesting that the inhibitory mechanism may involve the activation of the SAMHD1 deoxynucleotide triphosphohydrolase activity. Thus, our data show for the first time a direct relationship of SAMHD1 and inhibition of influenza replication, and provides perspectives for new studies on the signaling modulation, through cellular receptors, to induce proteins of great importance in the control of relevant infections for public health.

Keywords: chemokine receptors, gp120, influenza, virus restriction factors

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Authors: Matthew Arnaouti, Michael Baird, Gabrielle Cahill, Tamara Worlton, Michelle Joseph

Abstract:

Introduction: Over ten years after the 7.0 magnitude Earthquake struck the capital of Haiti, impacting over three million people and leading to the deaths of over two hundred thousand, the multinational humanitarian response remains the largest disaster relief effort to date. This study critically evaluates the multi-sector and multinational disaster response to the Earthquake, looking at how the lessons learned from this analysis can be applied to future disaster response efforts. We put particular emphasis on assessing the interaction between civilian and military sectors during this humanitarian relief effort, with the hopes of highlighting how concrete guidelines are essential to improve future responses. Methods: An extensive scoping review of the relevant literature was conducted - where library scientists conducted reproducible, verified systematic searches of multiple databases. Grey literature and hand searches were utilised to identify additional unclassified military documents, for inclusion in the study. More than 100 documents were included for data extraction and analysis. Key domains were identified, these included: Humanitarian and Military Response, Communication, Coordination, Resources, Needs Assessment and Pre-Existing Policy. Corresponding information and lessons-learned pertaining to these domains was then extracted - detailing the barriers and facilitators to an effective response. Results: Multiple themes were noted which stratified all identified domains - including the lack of adequate pre-existing policy, as well as extensive ambiguity of actors’ roles. This ambiguity was continually influenced by the complex role the United States military played in the disaster response. At a deeper level, the effects of neo-colonialism and concern about infringements on Haitian sovereignty played a substantial role at all levels: setting the pre-existing conditions and determining the redevelopment efforts that followed. Furthermore, external factors significantly impacted the response, particularly the loss of life within the political and security sectors. This was compounded by the destruction of important infrastructure systems - particularly electricity supplies and telecommunication networks, as well as air and seaport capabilities. Conclusions: This study stands as one of the first and most comprehensive evaluations, systematically analysing the civilian and military response - including their collaborative efforts. This study offers vital information for improving future combined responses and provides a significant opportunity for advancing knowledge in disaster relief efforts - which remains a more pressing issue than ever. The categories and domains formulated serve to highlight interdependent factors that should be applied in future disaster responses, with significant potential to aid the effective performance of humanitarian actors. Further studies will be grounded in these findings, particularly the need for greater inclusion of the Haitian perspective in the literature, through additional qualitative research studies.

Keywords: civilian and military collaboration, combined response, disaster, disaster response, earthquake, Haiti, humanitarian response

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

Abstract:

Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

Procedia PDF Downloads 97