Automated Sleep Apnea Quantification Based on Respiratory Movement

Obstructive sleep apnea (OSA) is a prevalent and treatable disorder of neurological and medical importance that is traditionally diagnosed through multi-channel laboratory polysomnography(PSG). However, OSA testing is increasingly performed with portable home devices using limited physiological channels. We tested the hypothesis that single channel respiratory effort alone could support automated quantification of apnea and hypopnea events. We developed a respiratory event detection algorithm applied to thoracic strain-belt data from patients with variable degrees of sleep apnea. We optimized parameters on a training set (n=57) and then tested performance on a validation set (n=59). The optimized algorithm correlated significantly with manual scoring in the validation set (R2 = 0.73 for training set, R2 = 0.55 for validation set; p0.92 and >0.85 using apnea-hypopnea index cutoff values of 5 and 15, respectively. Our findings demonstrate that manually scored AHI values can be approximated from thoracic movements alone. This finding has potential applications for automating laboratory PSG analysis as well as improving the performance of limited channel home monitors

Brain Age from the Electroencephalogram of Sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging

Automated information extraction from free-text EEG reports

In this study we have developed a supervised learning to automatically detect with high accuracy EEG reports that describe seizures and epileptiform discharges. We manually labeled 3,277 documents as describing one or more seizures vs no seizures, and as describing epileptiform discharges vs no epileptiform discharges. We then used Naïve Bayes to develop a system able to automatically classify EEG reports into these categories. Our system consisted of normalization techniques, extraction of key sentences, and automated feature selection using cross validation. As candidate features we used key words and special word patterns called elastic word sequences (EWS). Final feature selection was accomplished via sequential backward selection. We used cross validation to predict out of sample performance. Our automated feature selection procedure resulted in a classifier with 38 features for seizure detection, and 23 features for epileptiform discharge detection. The average [95% CI] area under the receiver operating curve was 99.05 [98.79, 99.32]% for detecting reports with seizures, and 96.15 [92.31, 100.00]% for detecting reports with epileptiform discharges. The methodology described herein greatly reduces the manual labor involved in identifying large cohorts of patients for retrospective neurophysiological studies of patients with epilepsy

Interchanging Interactive 3-d Graphics for Astronomy

We demonstrate how interactive, three-dimensional (3-d) scientific visualizations can be efficiently interchanged between a variety of mediums. Through the use of an appropriate interchange format, and a unified interaction interface, we minimize the effort to produce visualizations appropriate for undertaking knowledge discovery at the astronomer's desktop, as part of conference presentations, in digital publications or as Web content. We use examples from cosmological visualization to address some of the issues of interchange, and to describe our approach to adapting S2PLOT desktop visualizations to the Web. Supporting demonstrations are available at http://astronomy.swin.edu.au/s2plot/interchange/Comment: 10 pages, 7 figures, submitted to Publications of the Astronomical Society of Australia. v2. Revised title, revised figure 1, fixed typos, minor additions to future work sectio

Translation of immunomodulatory therapy to treat chronic heart failure: Preclinical studies to first in human

BACKGROUND: Inflammation has been associated with progression and complications of chronic heart failure (HF) but no effective therapy has yet been identified to treat this dysregulated immunologic state. The selective cytopheretic device (SCD) provides extracorporeal autologous cell processing to lessen the burden of inflammatory activity of circulating leukocytes of the innate immunologic system. AIM: The objective of this study was to evaluate the effects of the SCD as an extracorporeal immunomodulatory device on the immune dysregulated state of HF. HF. METHODS AND RESULTS: SCD treatment in a canine model of systolic HF or HF with reduced ejection fraction (HFrEF) diminished leukocyte inflammatory activity and enhanced cardiac performance as measured by left ventricular (LV) ejection fraction and stroke volume (SV) up to 4 weeks after treatment initiation. Translation of these observations in first in human, proof of concept clinical study was evaluated in a patient with severe HFrEFHFrEF ineligible for cardiac transplantation or LV LV assist device (LVAD) due to renal insufficiency and right ventricular dysfunction. Six hour SCD treatments over 6 consecutive days resulted in selective removal of inflammatory neutrophils and monocytes and reduction in key plasma cytokines, including tumor necrosis factor-alpha (TNF-α),), interleukin (IL)-6, IL-8, and monocyte chemoattractant protein (MCP)-1. These immunologic changes were associated with significant improvements in cardiac power output, right ventricular stroke work index, cardiac index and LVSV index…. Stabilization of renal function with progressive volume removal permitted successful LVAD implantation. CONCLUSION: This translational research study demonstrates a promising immunomodulatory approach to improve cardiac performance in HFrEFHFrEF and supports the important role of inflammation in the progression of HFHF

Big data in sleep medicine: prospects and pitfalls in phenotyping

Clinical polysomnography (PSG) databases are a rich resource in the era of “big data” analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea–hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine

Vascular Leak and Hypercytokinemia Associated with Severe Fever with Thrombocytopenia Syndrome Virus Infection in Mice

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging viral hemorrhagic fever (VHF) endemic to China, South Korea, Japan, and Vietnam. Here we characterize the pathogenesis and natural history of disease in IFNAR-/- mice challenged with the HB29 strain of SFTS virus (SFTSV) and demonstrate hallmark features of VHF such as vascular leak and high concentrations of proinflammatory cytokines in blood and tissues. Treatment with FX06, a natural plasmin digest product of fibrin in clinical development as a treatment for vascular leak, reduced vascular permeability associated with SFTSV infection but did not significantly improve survival outcome. Further studies are needed to assess the role of vascular compromise in the SFTS disease process modeled in IFNAR-/- mice

Modeling Severe Fever with Thrombocytopenia Syndrome Virus Infection in Golden Syrian Hamsters: Importance of STAT2 in Preventing Disease and Effective Treatment with Favipiravir

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease endemic in parts of Asia. The etiologic agent, SFTS virus (SFTSV; family Bunyaviridae, genus Phlebovirus) has caused significant morbidity and mortality in China, South Korea, and Japan, with key features of disease being intense fever, thrombocytopenia, and leukopenia. Case fatality rates are estimated to be in the 30% range, and no antivirals or vaccines are approved for use for treatment and prevention of SFTS. There is evidence that in human cells, SFTSV sequesters STAT proteins in replication complexes, thereby inhibiting type I interferon signaling. Here, we demonstrate that hamsters devoid of functional STAT2 are highly susceptible to as few as 10 PFU of SFTSV, with animals generally succumbing within 5 to 6 days after subcutaneous challenge. The disease included marked thrombocytopenia and inflammatory disease characteristic of the condition in humans. Infectious virus titers were present in the blood and most tissues 3 days after virus challenge, and severe inflammatory lesions were found in the spleen and liver samples of SFTSV-infected hamsters. We also show that SFTSV infection in STAT2 knockout (KO) hamsters is responsive to favipiravir treatment, which protected all animals from lethal disease and reduced serum and tissue viral loads by 3 to 6 orders of magnitude. Taken together, our results provide additional insights into the pathogenesis of SFTSV infection and support the use of the newly described STAT2 KO hamster model for evaluation of promising antiviral therapies. IMPORTANCE Severe fever with thrombocytopenia syndrome (SFTS) is an emerging viral disease for which there are currently no therapeutic options or available vaccines. The causative agent, SFTS virus (SFTSV), is present in China, South Korea, and Japan, and infections requiring medical attention result in death in as many as 30% of the cases. Here, we describe a novel model of SFTS in hamsters genetically engineered to be deficient in a protein that helps protect humans and animals against viral infections. These hamsters were found to be susceptible to SFTSV and share disease features associated with the disease in humans. Importantly, we also show that SFTSV infection in hamsters can be effectively treated with a broad-spectrum antiviral drug approved for use in Japan. Our findings suggest that the new SFTS model will be an excellent resource to better understand SFTSV infection and disease as well as a valuable tool for evaluating promising antiviral drugs