447 research outputs found

    Cross validation of bi-modal health-related stress assessment

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    This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

    Investigation of Mitochondrial Dysfunction by Sequential Microplate-Based Respiration Measurements from Intact and Permeabilized Neurons

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    Mitochondrial dysfunction is a component of many neurodegenerative conditions. Measurement of oxygen consumption from intact neurons enables evaluation of mitochondrial bioenergetics under conditions that are more physiologically realistic compared to isolated mitochondria. However, mechanistic analysis of mitochondrial function in cells is complicated by changing energy demands and lack of substrate control. Here we describe a technique for sequentially measuring respiration from intact and saponin-permeabilized cortical neurons on single microplates. This technique allows control of substrates to individual electron transport chain complexes following permeabilization, as well as side-by-side comparisons to intact cells. To illustrate the utility of the technique, we demonstrate that inhibition of respiration by the drug KB-R7943 in intact neurons is relieved by delivery of the complex II substrate succinate, but not by complex I substrates, via acute saponin permeabilization. In contrast, methyl succinate, a putative cell permeable complex II substrate, failed to rescue respiration in intact neurons and was a poor complex II substrate in permeabilized cells. Sequential measurements of intact and permeabilized cell respiration should be particularly useful for evaluating indirect mitochondrial toxicity due to drugs or cellular signaling events which cannot be readily studied using isolated mitochondria

    Head to head comparison of 2D vs real time 3D dipyridamole stress echocardiography

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    Real-time three-dimensional (RT-3D) echocardiography has entered the clinical practice but true incremental value over standard two-dimensional echocardiography (2D) remains uncertain when applied to stress echo. The aim of the present study is to establish the additional value of RT-3D stress echo over standard 2D stress echocardiography. We evaluated 23 consecutive patients (age = 65 ± 10 years, 16 men) referred for dipyridamole stress echocardiography with Sonos 7500 (Philips Medical Systems, Palo, Alto, CA) equipped with a phased – array 1.6–2.5 MHz probe with second harmonic capability for 2D imaging and a 2–4 MHz matrix-phased array transducer producing 60 × 70 volumetric pyramidal data containing the entire left ventricle for RT-3D imaging. In all patients, images were digitally stored in 2D and 3D for baseline and peak stress with a delay between acquisitions of less than 60 seconds. Wall motion analysis was interpreted on-line for 2D and off-line for RT-3D by joint reading of two expert stress ecocardiographist. Segmental image quality was scored from 1 = excellent to 5 = uninterpretable. Interpretable images were obtained in all patients. Acquisition time for 2D images was 67 ± 21 sec vs 40 ± 22 sec for RT-3D (p = 0.5). Wall motion analysis time was 2.8 ± 0.5 min for 2D and 13 ± 7 min for 3D (p = 0.0001). Segmental image quality score was 1.4 ± 0.5 for 2D and 2.6 ± 0.7 for 3D (p = 0.0001). Positive test results was found in 5/23 patients. 2D and RT-3D were in agreement in 3 out of these 5 positive exams. Overall stress result (positive vs negative) concordance was 91% (Kappa = 0.80) between 2D and RT-3D. During dipyridamole stress echocardiography RT-3D imaging is highly feasible and shows a high concordance rate with standard 2D stress echo. 2D images take longer time to acquire and RT-3D is more time-consuming to analyze. At present, there is no clear clinical advantage justifying routine RT-3D stress echocardiography use

    Quantitative imaging of concentrated suspensions under flow

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    We review recent advances in imaging the flow of concentrated suspensions, focussing on the use of confocal microscopy to obtain time-resolved information on the single-particle level in these systems. After motivating the need for quantitative (confocal) imaging in suspension rheology, we briefly describe the particles, sample environments, microscopy tools and analysis algorithms needed to perform this kind of experiments. The second part of the review focusses on microscopic aspects of the flow of concentrated model hard-sphere-like suspensions, and the relation to non-linear rheological phenomena such as yielding, shear localization, wall slip and shear-induced ordering. Both Brownian and non-Brownian systems will be described. We show how quantitative imaging can improve our understanding of the connection between microscopic dynamics and bulk flow.Comment: Review on imaging hard-sphere suspensions, incl summary of methodology. Submitted for special volume 'High Solid Dispersions' ed. M. Cloitre, Vol. xx of 'Advances and Polymer Science' (Springer, Berlin, 2009); 22 pages, 16 fig

    Metastatic group 3 medulloblastoma is driven by PRUNE1 targeting NME1-TGF-β-OTX2-SNAIL via PTEN inhibition.

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    Genetic modifications during development of paediatric groups 3 and 4 medulloblastoma are responsible for their highly metastatic properties and poor patient survival rates. PRUNE1 is highly expressed in metastatic medulloblastoma group 3, which is characterized by TGF-β signalling activation, c-MYC amplification, and OTX2 expression. We describe the process of activation of the PRUNE1 signalling pathway that includes its binding to NME1, TGF-β activation, OTX2 upregulation, SNAIL (SNAI1) upregulation, and PTEN inhibition. The newly identified small molecule pyrimido-pyrimidine derivative AA7.1 enhances PRUNE1 degradation, inhibits this activation network, and augments PTEN expression. Both AA7.1 and a competitive permeable peptide that impairs PRUNE1/NME1 complex formation, impair tumour growth and metastatic dissemination in orthotopic xenograft models with a metastatic medulloblastoma group 3 cell line (D425-Med cells). Using whole exome sequencing technology in metastatic medulloblastoma primary tumour cells, we also define 23 common 'non-synonymous homozygous' deleterious gene variants as part of the protein molecular network of relevance for metastatic processes. This PRUNE1/TGF-β/OTX2/PTEN axis, together with the medulloblastoma-driver mutations, is of relevance for future rational and targeted therapies for metastatic medulloblastoma group 3

    Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance

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    BACKGROUND: Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC). METHODS: We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real). RESULTS: Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets. CONCLUSION: We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results

    Fast MCMC sampling for hidden markov models to determine copy number variations

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    <p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC) sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times; datasets coming from high-density arrays and next generation sequencing amplify these problems.</p> <p>Results</p> <p>We propose an approximate sampling technique, inspired by compression of discrete sequences in HMM computations and by <it>kd</it>-trees to leverage spatial relations between data points in typical data sets, to speed up the MCMC sampling.</p> <p>Conclusions</p> <p>We test our approximate sampling method on simulated and biological ArrayCGH datasets and high-density SNP arrays, and demonstrate a speed-up of 10 to 60 respectively 90 while achieving competitive results with the state-of-the art Bayesian approaches.</p> <p><it>Availability: </it>An implementation of our method will be made available as part of the open source GHMM library from <url>http://ghmm.org</url>.</p
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