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Feline infectious peritonitis (FIP) is a fatal disease of cats, and a sequela of systemic feline coronavirus (FCoV) infection. Mutations in the viral spike (S) gene have been associated with FCoVs found in tissues from cats with FIP, but not FCoVs found in faeces from healthy cats, and are implicated in monocyte/macrophage tropism and systemic spread. This study was designed to determine whether S gene mutation analysis can reliably diagnose FIP. Cats were categorised as with FIP ( n  = 57) or without FIP ( n  = 45) based on gross post-mortem and histopathological examination including immunohistochemistry for FCoV antigen. RNA was purified from available tissue, fluid and faeces. Reverse-transcriptase quantitative-PCR (RT-qPCR) was performed on all samples using FCoV-specific primers, followed by sequencing of a section of the S gene on RT-qPCR positive samples. Samples were available from a total of 102 cats. Tissue, fluid, and faecal samples from cats with FIP were more likely to be FCoV RT-qPCR-positive (90.4, 78.4 and 64.6% respectively) than those from cats without FIP (7.8, 2.1 and 20% respectively). Identification of S gene mutated FCoVs as an additional step to the detection of FCoV alone, only moderately increased specificity for tissue samples (from 92.6 to 94.6%) but specificity was unchanged for fluid samples (97.9%) for FIP diagnosis; however, sensitivity was markedly decreased for tissue (from 89.8 to 80.9%) and fluid samples (from 78.4 to 60%) for FIP diagnosis. These findings demonstrate that S gene mutation analysis in FCoVs does not substantially improve the ability to diagnose FIP as compared to detection of FCoV alone.

Electromagnetic (EM) medical technologies are rapidly expanding worldwide for both diagnostics and therapeutics. As these technologies are low-cost and minimally invasive, they have been the focus of significant research efforts in recent years. Such technologies are often based on the assumption that there is a contrast in the dielectric properties of different tissue types or that the properties of particular tissues fall within a defined range. Thus, accurate knowledge of the dielectric properties of biological tissues is fundamental to EM medical technologies. Over the past decades, numerous studies were conducted to expand the dielectric repository of biological tissues. However, dielectric data is not yet available for every tissue type and at every temperature and frequency. For this reason, dielectric measurements may be performed by researchers who are not specialists in the acquisition of tissue dielectric properties. To this end, this paper reviews the tissue dielectric measurement process performed with an open-ended coaxial probe. Given the high number of factors, including equipment- and tissue-related confounders, that can increase the measurement uncertainty or introduce errors into the tissue dielectric data, this work discusses each step of the coaxial probe measurement procedure, highlighting common practices, challenges, and techniques for controlling and compensating for confounders.

The dielectric properties of biological tissues are typically measured using the open-ended coaxial probe technique, which is based on the assumption that the tissue sample is homogeneous. Therefore, for heterogeneous tissue samples, additional post-measurement sample processing is conducted. Specifically, post-measurement histological analysis may be performed in order to associate the measured dielectric properties with the tissue types present in a heterogeneous sample. Accurate post-measurement histological analysis enables identification of the constituent tissue types that contributed to the measured dielectric properties, and their relative distributions. There is no standard protocol for conducting post-measurement histological analysis, which leads to high numbers of excluded tissue samples and inconsistencies in the resulting reported data for heterogeneous tissues. To this extent, this study examines the post-measurement histological process and the challenges in associating the acquired dielectric properties with the different tissue types present in heterogeneous samples. The results demonstrate that the histological process inevitably alters the morphology of samples, thus introducing errors in the interpretation of the dielectric properties acquired from heterogeneous biological samples. Notably, sample size was seen to shrink by up to 90% through the histological process, meaning that sensing volume determined from fresh tissues is not directly applicable to histology images.

Osteoporosis is one of the most common diseases that leads to bone fractures. Dual-energy X-ray absorptiometry is currently employed to measure the bone mineral density and to diagnose osteoporosis. Alternatively, the dielectric properties of bones are found to be influenced by bone mineral density; hence, dielectric properties of bones may potentially be used to diagnose osteoporosis. Microwave tomographic imaging is currently in development to potentially measure in vivo dielectric properties of bone. Therefore, the foci of this work are to summarize all available dielectric data of bone in the microwave frequency range and to analyze the confounders that may have resulted in variations in reported data. This study also compares the relationship between the dielectric properties and bone quality reported across different studies. The review suggests that variations exist in the dielectric properties of bone and the relationship between bone volume fraction and dielectric properties is in agreement across all studies. Conversely, the evidence of a relationship between bone mineral density and dielectric properties is inconsistent across the studies. This summary of dielectric data of bone along with a comparison of the relationship between the dielectric properties and bone quality will accelerate the development of microwave tomographic imaging devices for the monitoring of osteoporosis.

Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations.

Brain haemorrhages often require urgent treatment with a consequent need for quick and accurate diagnosis. Therefore, in this study, we investigate Support Vector Machine (SVM) classifiers for detecting brain haemorrhages using Electrical Impedance Tomography (EIT) measurement frames. A 2-layer model of the head, along with a series of haemorrhages, is designed as both numerical models and physical phantoms. EIT measurement frames, taken from an electrode array placed on the head surface, are used to train and test linear SVM classifiers. Various scenarios are implemented on both platforms to examine the impact of variables such as noise level, lesion location, lesion size, variation in electrode positioning, and variation in anatomy, on the classifier performance. The classifier performed well in numerical models (sensitivity and specificity of 90%+) with signal-to-noise ratios of 60 dB+, was independent of lesion location, and could detect lesions reliably down to the tested minimum volume of 5 ml. Slight variations in electrode layout did not affect performance. Performance was affected by variations in anatomy however, emphasising the need for large training sets covering different anatomies. The phantom models proved more challenging, with maximal sensitivity and specificity of 75% when used with the linear SVM. Finally, the performance of two more complex classifiers is briefly examined and compared to the linear SVM classifier. These results demonstrate that a radial basis function (RBF) SVM classifier and a neural network classifier can improve detection accuracy. Classifiers applied to EIT measurement frames is a novel approach for lesion detection and may offer an effective diagnostic tool clinically. A challenge is to translate the strong results from numerical models into real world phantoms and ultimately human patients, as well as the selection and development of optimal classifiers for this application.

The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added.

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.

Although RNA interference (RNAi) has become a ubiquitous laboratory tool since its discovery 12 years ago, in vivo delivery to selected cell types remains a major technical challenge. Here, we report the use of lentiviral vectors for long-term in vivo delivery of RNAi selectively to resident alveolar macrophages (AMs), key immune effector cells in the lung. We demonstrate the therapeutic potential of this approach by RNAi-based downregulation of p65 (RelA), a component of the pro-inflammatory transcriptional regulator, nuclear factor κB (NF-κB) and a key participant in lung disease pathogenesis. In vivo RNAi delivery results in decreased induction of NF-κB and downstream neutrophilic chemokines in transduced AMs as well as attenuated lung neutrophilia following stimulation with lipopolysaccharide (LPS). Through concurrent delivery of a novel lentiviral reporter vector (lenti-NF-κB-luc-GFP) we track in vivo expression of NF-κB target genes in real time, a critical step towards extending RNAi-based therapy to longstanding lung diseases. Application of this system reveals that resident AMs persist in the airspaces of mice following the resolution of LPS-induced inflammation, thus allowing these localized cells to be used as effective vehicles for prolonged RNAi delivery in disease settings.

We have used the pig, a large natural host animal for influenza with many physiological similarities to humans, to characterize αβ, γδ T cell and antibody (Ab) immune responses to the 2009 pandemic H1N1 virus infection. We evaluated the kinetic of virus infection and associated response in inbred Babraham pigs with identical MHC (Swine Leucocyte Antigen) and compared them to commercial outbred animals. High level of nasal virus shedding continued up to days 4 to 5 post infection followed by a steep decline and clearance of virus by day 9. Adaptive T cell and Ab responses were detectable from days 5 to 6 post infection reaching a peak at 9 to 14 days. γδ T cells produced cytokines ex vivo at day 2 post infection, while virus reactive IFNγ producing γδ T cells were detected from day 7 post infection. Analysis of NP tetramer specific and virus specific CD8 and CD4 T cells in blood, lung, lung draining lymph nodes, and broncho-alveolar lavage (BAL) showed clear differences in cytokine production between these tissues. BAL contained the most highly activated CD8, CD4, and γδ T cells producing large amounts of cytokines, which likely contribute to elimination of virus. The weak response in blood did not reflect the powerful local lung immune responses. The immune response in the Babraham pig following H1N1pdm09 influenza infection was comparable to that of outbred animals. The ability to utilize these two swine models together will provide unparalleled power to analyze immune responses to influenza.