ß-Glucan-Induced Immuno-Modulation: A Role for the Intestinal Microbiota and Short-Chain Fatty Acids in Common Carp.

Dietary supplementation of fish with ß-glucans has been commonly associated with immunomodulation and generally accepted as beneficial for fish health. However, to date the exact mechanisms of immunomodulation by ß-glucan supplementation in fish have remained elusive. In mammals, a clear relation between high-fibre diets, such as those including ß-glucans, and diet-induced immunomodulation via intestinal microbiota and associated metabolites has been observed. In this study, first we describe by 16S rRNA sequencing the active naive microbiota of common carp intestine. Based on the abundance of the genus Bacteroides, well known for their capacity to degrade and ferment carbohydrates, we hypothesize that common carp intestinal microbiota could ferment dietary ß-glucans. Indeed, two different ß-glucan preparations (curdlan and MacroGard®) were both fermented in vitro, albeit with distinct fermentation dynamics and distinct production of short-chain fatty acids (SCFA). Second, we describe the potential immunomodulatory effects of the three dominant SCFAs (acetate, butyrate, and propionate) on head kidney leukocytes, showing effects on both nitric oxide production and expression of several cytokines (il-1b, il-6, tnfα, and il-10) in vitro. Interestingly, we also observed a regulation of expression of several gpr40L genes, which were recently described as putative SCFA receptors. Third, we describe how a single in vivo oral gavage of carp with MacroGard® modulated simultaneously, the expression of several pro-inflammatory genes (il-1b, il-6, tnfα), type I IFN-associated genes (tlr3.1, mx3), and three specific gpr40L genes. The in vivo observations provide indirect support to our in vitro data and the possible role of SCFAs in ß-glucan-induced immunomodulation. We discuss how ß-glucan-induced immunomodulatory effects can be explained, at least in part, by fermentation of MacroGard® by specific bacteria, part of the naive microbiota of common carp intestine, and how a subsequent production of SFCAs could possibly explain immunomodulation by ß-glucan via SCFA receptors present on leukocytes.

EpCAM-Binding DARPins for Targeted Photodynamic Therapy of Ovarian Cancer.

Ovarian cancer is the most lethal gynecological malignancy due to late detection associated with dissemination throughout the abdominal cavity. Targeted photodynamic therapy (tPDT) aimed at epithelial cell adhesion molecule (EpCAM), overexpressed in over 90% of ovarian cancer metastatic lesions, is a promising novel therapeutic modality. Here, we tested the specificity and activity of conjugates of EpCAM-directed designed ankyrin repeat proteins (DARPins) with the photosensitizer IRDye 700DX in in vitro and in vivo ovarian cancer models. EpCAM-binding DARPins (Ec1: Kd = 68 pM; Ac2: Kd = 130 nM) and a control DARPin were site-specifically functionalized with fluorophores or IRDye 700DX. Conjugation of anti-EpCAM DARPins with fluorophores maintained EpCAM-specific binding in cell lines and patient-derived ovarian cancer explants. Penetration of DARPin Ec1 into tumor spheroids was slower than that of Ac2, indicative of a binding site barrier effect for Ec1. DARPin-IRDye 700DX conjugates killed EpCAM-expressing cells in a highly specific and illumination-dependent fashion in 2D and 3D cultures. Furthermore, they effectively homed to EpCAM-expressing subcutaneous OV90 xenografts in mice. In conclusion, the high activity and specificity observed in preclinical ovarian cancer models, combined with a high specificity in patient material, warrant a further investigation of EpCAM-targeted PDT for ovarian cancer.

The Role of Baroreflex Sensitivity in Acute Hypotensive Episodes Prediction in the Intensive Care Unit.

A life threatening condition in Intensive Care Unit (ICU) is the Acute Hypotensive Episode (AHE). Patients experiencing an AHE may suffer from irreversible organ damage associated with increased mortality. Predicting the onset of AHE could be of pivotal importance to establish appropriate and timely interventions. We propose a method that, using waveforms widely acquired in ICU, like Arterial Blood Pressure (ABP) and Electrocardiogram (ECG), will extract features relative to the cardiac system to predict whether or not a patient will experience a hypotensive episode. Specifically, we want to assess if there are hidden patterns in the dynamics of baroreflex able to improve the prediction of AHEs. We will investigate the predictive power of features related to the baroreflex by performing classifications with and without them. Results are obtained using 17 classifiers belonging to different model families: classification trees, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs) replicated with different set of hyper-parameters and logistic regression. On average, the use of baroreflex features in the AHE prediction process increases the Area Under the Curve (AUC) by 10%.

Association between fluid balance and survival in critically ill patients.

OBJECTIVE: Although the consequences of chronic fluid retention are well known, those of iatrogenic fluid retention that occurs during critical illness have not been fully determined. Therefore, we investigated the association between fluid balance and survival in a cohort of almost 16,000 individuals who survived an intensive care unit (ICU) stay in a large, urban, tertiary medical centre. DESIGN: Longitudinal analysis of fluid balance at ICU discharge and 90-day post-ICU survival. MEASUREMENTS: Associations between fluid balance during the ICU stay, determined from the electronic bedside record, and survival were tested using Cox proportional hazard models adjusted for severity of critical illness. RESULTS: There were 1827 deaths in the first 90 days after ICU discharge. Compared with the lowest quartile of discharge fluid balance [median (interquartile range) -1.5 (-3.1, -0.7) L], the highest quartile [7.6 (5.7, 10.8) L] was associated with a 35% [95% confidence interval (CI) 1.13-1.61)] higher adjusted risk of death. Fluid balance was not associated with outcome amongst individuals without congestive heart failure or renal dysfunction. Amongst patients with either comorbidity, however, fluid balance was strongly associated with outcome, with the highest quartile having a 55% (95% CI 1.24-1.95) higher adjusted risk of death than the lowest quartile. Isotonic fluid balance, defined as the difference between intravenous isotonic fluid administration and urine output, was similarly associated with 90-day outcomes. CONCLUSION: Positive fluid balance at the time of ICU discharge is associated with increased risk of death, after adjusting for markers of illness severity and chronic medical conditions, particularly in patients with underlying heart or kidney disease. Restoration of euvolaemia prior to discharge may improve survival after acute illness.

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter.

Physiological signals such as the electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of the heart rate (HR) and ABP. A robust HR estimation method is described that compensates for these problems. The method is based upon the concept of fusing multiple signal quality indices (SQIs) and HR estimates derived from multiple electrocardiogram (ECG) leads and an invasive ABP waveform recorded from ICU patients. Physiological SQIs were obtained by analyzing the statistical characteristics of each waveform and their relationships to each other. HR estimates from the ECG and ABP are tracked with separate Kalman filters, using a modified update sequence based upon the individual SQIs. Data fusion of each HR estimate was then performed by weighting each estimate by the Kalman filters' SQI-modified innovations. This method was evaluated on over 6000 h of simultaneously acquired ECG and ABP from a 437 patient subset of ICU data by adding real ECG and realistic artificial ABP noise. The method provides an accurate HR estimate even in the presence of high levels of persistent noise and artifact, and during episodes of extreme bradycardia and tachycardia.

Cycle-averaged dynamics of a periodically driven, closed-loop circulation model.

Time-varying elastance models have been used extensively in the past to simulate the pulsatile nature of cardiovascular waveforms. Frequently, however, one is interested in dynamics that occur over longer time scales, in which case a detailed simulation of each cardiac contraction becomes computationally burdensome. In this paper, we apply circuit-averaging techniques to a periodically driven, closed-loop, three-compartment recirculation model. The resultant cycle-averaged model is linear and time invariant, and greatly reduces the computational burden. It is also amenable to systematic order reduction methods that lead to further efficiencies. Despite its simplicity, the averaged model captures the dynamics relevant to the representation of a range of cardiovascular reflex mechanisms.

Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure.

The paper presents an algorithm for reducing false alarms related to changes in arterial blood pressure (ABP) in intensive care unit (ICU) monitoring. The algorithm assesses the ABP signal quality, analyses the relationship between the electrocardiogram and ABP using a fuzzy logic approach and post-processes (accepts or rejects) ABP alarms produced by a commercial monitor. The algorithm was developed and evaluated using unrelated sets of data from the MIMIC database. By rejecting 98.2% (159 of 162) of the false ABP alarms produced by the monitor using the test set of data, the algorithm was able to reduce the false ABP alarm rate from 26.8% to 0.5% of ABP alarms, while accepting 99.8% (441 of 442) of true ABP alarms. The results show that the algorithm is effective and practical, and its use in future patient monitoring systems is feasible.

Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia.

The long-term ST database is the result of a multinational research effort. The goal was to develop a challenging and realistic research resource for development and evaluation of automated systems to detect transient ST segment changes in electrocardiograms and for supporting basic research into the mechanisms and dynamics of transient myocardial ischaemia. Twenty-four hour ambulatory ECG records were selected from routine clinical practice settings in the USA and Europe, between 1994 and 2000, on the basis of occurrence of ischaemic and non-ischaemic ST segment changes. Human expert annotators used newly developed annotation protocols and a specially developed interactive graphic editor tool (SEMIA) that supported paperless editing of annotations and facilitated international co-operation via the Internet. The database contains 86 two- and three-channel 24 h annotated ambulatory records from 80 patients and is stored on DVD-ROMs. The database annotation files contain ST segment annotations of transient ischaemic (1155) and heart-rate related ST episodes and annotations of non-ischaemic ST segment events related to postural changes and conduction abnormalities. The database is intended to complement the European Society of Cardiology ST-T database and the MIT-BIH and AHA arrhythmia databases. It provides a comprehensive representation of 'real-world' data, with numerous examples of transient ischaemic and non-ischaemic ST segment changes, arrhythmias, conduction abnormalities, axis shifts, noise and artifacts.

An automated, web-enabled and searchable database system for archiving electrogram and related data from implantable cardioverter defibrillators.

The advent of implantable cardioverter defibrillators (ICDs) has resulted in significant reductions in mortality in patients at high risk for sudden cardiac death. Extensive related basic research and clinical investigation continue. ICDs typically record intracardiac electrograms and inter-beat intervals along with device settings during episodes of device delivery of therapy. Researchers wishing to study these data further have until now been limited to viewing paper plots. In support of multi-center clinical studies of patients with ICDs, we have developed a web based searchable ICD data archiving system, which allows users to use a web browser to upload ICD data from diskettes to a server where the data are automatically processed and archived. Users can view and download the archived ICD data directly via the web. The entire system is built from open source software. At present more than 500 patient ICD data sets have been uploaded to and archived in the system. This project will be of value not only to those who wish to conduct research using ICD data, but also to clinicians who need to archive and review ICD data collected from their patients.

A computational model-based validation of Guyton's analysis of cardiac output and venous return curves.

Guyton developed a popular approach for understanding the factors responsible for cardiac output (CO) regulation in which 1) the heart-lung unit and systemic circulation are independently characterized via CO and venous return (VR) curves, and 2) average CO and right atrial pressure (RAP) of the intact circulation are predicted by graphically intersecting the curves. However, this approach is virtually impossible to verify experimentally. We theoretically evaluated the approach with respect to a nonlinear, computational model of the pulsatile heart and circulation. We developed two sets of open circulation models to generate CO and VR curves, differing by the manner in which average RAP was varied. One set applied constant RAPs, while the other set applied pulsatile RAPs. Accurate prediction of intact, average CO and RAP was achieved only by intersecting the CO and VR curves generated with pulsatile RAPs because of the pulsatility and nonlinearity (e.g., systemic venous collapse) of the intact model. The CO and VR curves generated with pulsatile RAPs were also practically independent. This theoretical study therefore supports the validity of Guyton's graphical analysis.

MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring.

Development and evaluation of Intensive Care Unit (ICU) decision-support systems would be greatly facilitated by the availability of a large-scale ICU patient database. Following our previous efforts with the MIMIC (Multi-parameter Intelligent Monitoring for Intensive Care) Database, we have leveraged advances in networking and storage technologies to develop a far more massive temporal database, MIMIC II. MIMIC II is an ongoing effort: data is continuously and prospectively archived from all ICU patients in our hospital. MIMIC II now consists of over 800 ICU patient records including over 120 gigabytes of data and is growing. A customized archiving system was used to store continuously up to four waveforms and 30 different parameters from ICU patient monitors. An integrated user-friendly relational database was developed for browsing of patients' clinical information (lab results, fluid balance, medications, nurses' progress notes). Based upon its unprecedented size and scope, MIMIC II will prove to be an important resource for intelligent patient monitoring research, and will support efforts in medical data mining and knowledge-discovery.

Introduction of computational models to PhysioNet.

PhysioNet is a national research resource that provides experimental data sets and open-source software for their analysis. Computational modeling can complement studies of these experimental data sets so as to facilitate the advancement of physiologic research. Thus, in order to introduce computational models to PhysioNet, we have developed and posted a cardiovascular model designed for research that generates reasonable human pulsatile hemodynamic waveforms, cardiac output and venous return curves, and beat-to-beat variability. Some of the key features of the software include: 1) compatibility with PhysioNet's open-source data analysis software; 2) online viewing and parameter updating as the data are being calculated; 3) off-line viewing after completion of the simulation; 4) pre-compiled Linux binaries; 5) open-source code that may be compiled on other platforms; and 6) an extensive user's manual and software guide.

Do nonlinearities play a significant role in short term, beat-to-beat variability?

Numerous studies of short-term beat-to-beat variability in cardiovascular signals have not resolved the debate about the completeness of linear analysis techniques. This aim of this paper is to evaluate further the role of nonlinearities in short-term, beat-to-beat variability. We compared linear autoregressive moving average (ARMA) and nonlinear neural network (NN) models for predicting instantaneous heart rate (HR) and mean arterial blood pressure (BP) from past HR and BP. To evaluate these models, we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that ARMA linear analysis techniques provide adequate characterization of the system dynamics responsible for generating short-term, beat-to-beat variability.

Efficient hemodynamic event detection utilizing relational databases and wavelet analysis.

Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.

Model-based parameter estimation using cardiovascular response to orthostatic stress.

This paper presents a cardiovascular model that is capable of simulating the short-term (< or approximately equal to 3 min) transient hemodynamic response to gravitational stress and a gradient-based optimization method that allows for the automated estimation of model parameters from simulated or experimental data. We perform a sensitivity analysis of the transient heart rate response to determine which parameters of the model impact the heart rate dynamics significantly. We subsequently include only those parameters in the estimation routine that impact the transient heart rate dynamics substantially. We apply the estimation algorithm to both simulated and real data and showed that restriction to the 20 most important parameters does not impair our ability to match the data.

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.

The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.

PhysioNet: a research resource for studies of complex physiologic and biomedical signals.

PhysioNet (http://www.physionet.org/) is a web-based resource supplying well-characterized physiologic signals and related open-source software to the biomedical research community. Inaugurated in September 1999 under the auspices of the NIH's National Center for Research Resources (NCRR), PhysioNet provides an on-line forum for free dissemination and exchange of research data and software, with facilities for cooperative analysis of data and evaluation of new analytic methods. As of September 2000, PhysioBank, the data archive made available via PhysioNet, contained roughly 35 gigabytes of recorded signals and annotations. PhysioNet is a public service of the Research Resource for Complex Physiologic Signals, a cooperative project initiated by researchers at Boston's Beth Israel Deaconess Medical Center/Harvard Medical School, Boston University, McGill University, and MIT.

Multiparameter trend monitoring and intelligent displays using wavelet analysis.

An Intelligent Patient Monitoring (IPM) framework is defined for the analysis and display of multiparameter trends from ICU patients. Wavelet analysis was utilized for detection of physiological events and artifacts in long-term trends. A group of 58 patients from the MIMIC database were identified in which the heart rate (HR), arterial blood pressure (ABP), and pulmonary artery pressure (PAP) were monitored. An estimated cardiac output (CO) signal, using HR and ABP, was shown to correlate strongly (r=.67) with actual CO measurements. Using wavelet analysis, automated artifact and physiological event detection algorithms were developed to monitor left ventricular hemodynamic function. Finally, an intelligent display system is presented for presentation of the data in ICU monitors.