74 research outputs found
EpiSleeve: Multimodal Night-time Seizure Detection
This project involves the creation of a nighttime wearable device that can measure heart rate, blood oxygen saturation, temperature, motion, and skin resistivity using sensors placed on the upper and lower arm. Incorporating a wide variety of sensors allows for detection of focal and generalized epilepsy. These sensors will be used to collect data to wirelessly (via Bluetooth) transmit to a separate base station for processing to determine if a seizure has occurred. If a seizure is detected for a specified period of time, the base station can call (via cellular communications) for medical aid to prevent harm to users. Once this device is validated, the technology will help users track seizures better and grant peace of mind if a seizure were to occur.https://commons.case.edu/intersections-fa20/1037/thumbnail.jp
Holter Monitoring in Clinically Healthy Cavalier King Charles Spaniels, Wire‐Haired Dachshunds, and Cairn Terriers
A detailed investigation of the porcine skin and nose microbiome using universal and <i>Staphylococcus</i> specific primers
Abstract MRSA is an increasing problem in humans as well as livestock. The bacterial co-colonization of the skin in MRSA carriers has been poorly investigated and moreover, there have been no methods for high resolution investigations of the Staphylococcus genus apart from tediously culturing or doing multiple PCRs. On 120 samples from pig ear, skin and nose, we generated amplicons from the V1-V2 region of the 16S rRNA gene to gather an overview of the genus-level microbiome, along with using MRSA specific plates to count MRSA. In parallel with this, amplicons of the tuf gene were generated, targeting only a region of the tuf gene found only in the Staphylococcus genus. Using these methods, we determined a core microbiota across the healthy pig and determined the Staphylococcus genus to be dominated by S. equorum. Moreover, we found Streptococcus to be inversely associated with Staphylococcus and MRSA, suggesting a role for this genus in combating MRSA. In this work, we have thoroughly investigated the skin and nose microbiome of the pig and developed a high throughput method for profiling the Staphylococcus genus which we believe will be useful for further investigations
Decreased flow-mediated vasodilation indicates endothelial dysfunction in dogs with severe stages of myxomatous mitral valve disease
A detailed investigation of the porcine skin and nose microbiome using universal and <i>Staphylococcus</i> specific primers
Abstract MRSA is an increasing problem in humans as well as livestock. The bacterial co-colonization of the skin in MRSA carriers has been poorly investigated and moreover, there have been no methods for high resolution investigations of the Staphylococcus genus apart from tediously culturing or doing multiple PCRs. On 120 samples from pig ear, skin and nose, we generated amplicons from the V1-V2 region of the 16S rRNA gene to gather an overview of the genus-level microbiome, along with using MRSA specific plates to count MRSA. In parallel with this, amplicons of the tuf gene were generated, targeting only a region of the tuf gene found only in the Staphylococcus genus. Using these methods, we determined a core microbiota across the healthy pig and determined the Staphylococcus genus to be dominated by S. equorum. Moreover, we found Streptococcus to be inversely associated with Staphylococcus and MRSA, suggesting a role for this genus in combating MRSA. In this work, we have thoroughly investigated the skin and nose microbiome of the pig and developed a high throughput method for profiling the Staphylococcus genus which we believe will be useful for further investigations
417 Design and rationale of a phase 1 study evaluating AMG 256, a novel, targeted, IL-21 receptor agonist and anti-PD-1 antibody, in patients with advanced solid tumors
High maximum heart rate in dogs with syncope and heart failure caused by myxomatous mitral valve disease
Decreased flow-mediated vasodilation in dogs with moderate severe myxomatous mitral valve disease
Tree identity rather than tree diversity drives earthworm communities in European forests
Given the key role of belowground biota on forest ecosystem functioning, it is important to identify the factors that influence their abundance and composition. However, the understanding of the ecological linkage between tree diversity and belowground biota is still insufficient. Here we investigated the influence of tree diversity (richness, True Shannon diversity index, functional diversity) and identity (proportion of evergreen leaf litter and leaf litter quality) on earthworm species richness and biomass at a continental and regional scale, using data from a Europe-wide forest research platform (FunDivEUROPE) spanning six major forest types. We found a marked tree identity effect at the continental scale, with proportion of evergreen leaf litter negatively affecting total earthworm biomass and species richness, as well as their biomass per functional group. Furthermore, there were clear litter quality effects with a latitudinal variation in trait-specific responses. In north and central Europe, earthworm biomass and species richness clearly increased with increasing litter nutrient concentrations (decreasing C:N ratio and increasing calcium concentration), whereas this influence of litter nutrients was absent or even reversed in southern Europe. In addition, although earthworms were unaffected by the number of tree species, tree diversity positively affected earthworm biomass at the continental scale through functional diversity of the leaf litter. By focusing on tree leaf litter traits, this study advanced our understanding of the mechanisms driving tree identity effects and supported previous findings that litter quality, as a proxy of tree identity, was a stronger driver of earthworm species richness and biomass than tree diversit
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Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder
Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis
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