SNaPshot, a primer-extension-based method, a multiplex of 25 SNPs in a single reaction has been practiced for reducing the expense of genotyping a panel of SNPs useful for identity control.Beef traceability has become mandatory in many regions of the world and is typically achieved through the use of unique numerical codes on ear tags and animal passports. DNA-based traceability uses the animal's own DNA code to identify it and the products derived from it. Using SNaPshot's cost, we introduced the Perl script SNPmplexViewer, which facilitates the analysis of trace files for reactions performed without the use of fluorescent size standards. SNPmplexViewer automatically aligns reference and target trace electropherograms, run with and without fluorescent size standards, respectively. SNPmplexViewer produces a modified target trace file containing a normalised trace in which the reference size standards are embedded. SNPmplexViewer also outputs aligned images of the two electropherograms together with a difference profile.To further decrease SNPmplexViewer enable genotyping of SnaPshot reactions performed without fluorescent size standards, using common fragment-sizing software packages. SNPmplexViewer's normalised output may also improve the genotyping software's performance. Thus, SNPmplexViewer is a general free tool enabling the reduction of SNaPshot's cost as well as the fast viewing and comparing of trace electropherograms for fragment analysis. SNPmplexViewer is available at http://cowry.agri.huji.ac.il/cgi-bin/SNPmplexViewer.cgi.Modified trace files generated by  Fragment analysis refers to any analysis of genetic markers which relies on variations in the length of a specific DNA sequence to indicate the presence or absence of certain marker alleles. Fragment analyses include, among others, the detection and genotyping of variations in short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) using fluorescently labelled DNA fragments .loci. Twelve STR loci are recommended by the International Society for Animal Genetics (ISAG) for routine use in bovine parentage testing and identification [loci recommended by the Food and Agriculture Organization of the United Nations (FAO) for genetic studies of domestic animals [SNaPshot, which is a primer-extension-based method, has been used to multiplex 25 SNPs for traceability assay in cattle [DNA-based traceability provides the most reliable way to link a source carcass to meat products by matching genetic marker profiles . Efficiefication . Six add animals , are opt animals . SNPs arSnaPshot fragment analysis estimates fragment size relative to fluorescently labelled DNA fragments of known length. The DNA fragments are electrophoresed under denaturing conditions in a matrix, which enables size separation at single-nucleotide resolution. The size standards combined with the samples of interest co-migrate in the electrophoretic system, undergo the same electrophoretic forces and provide the necessary data to generate a calibration curve. Based on this curve, the size of unknown fragments in the experimental sample is determined by fragment analysis software [software .SeqDoC algorithm [SNaPshot assay for cattle traceability [Roughly one-half of the cost of automated genotyping is due to the fluorescently labelled internal size standards, which have relatively few vendors . Hence, lgorithm , alignmeeability .SNPmplexViewer is based on the algorithm implemented in SeqDoC [SeqDoC and an additional step that creates a modified target trace file containing a normalised trace in which the reference size standards are embedded. Each of the steps was modified to handle the FSA format used for fragment analysis, which is more complex than the ABI format handled by SeqDoC. As the ABI format is a subtype of the FSA format, SNPmplexViewer is compatible with both formats.n SeqDoC . It follTwo trace files (FSA or ABI formats), one a reference and the other the target trace, are the main user input. These may be indicated from the command line or uploaded via the web form in the on-line version. The channel data and other relevant data are extracted using the Perl ABIF.pm module . Trace dSNPmplexViewer takes an approach that differs from that of SeqDoC, which does consider the average values of signal and noise for each of the channels. To obtain these values, our algorithm first calculates the average signal of all points. Then it estimates the noise to be the mean value of all points that are below the signal average of all points. Subtracting this value from the signal average of all points gives the average data signal.Since raw data are differently represented by the different sequencer models, data are first transformed according to the sequencer model by either removing control numbers or applying a matrix that accounts for the overlaps in the dye spectrum. To correctly align trace runs with strong and weaker signals, it is necessary to normalise the trace values of the four data channels of the dye-labelled fragments to be sized and remove blank data at the beginning of the trace. To achieve this, Normalisation is performed for each channel individually, and sets all of the data points that are below the estimated noise value to zero. The other data points are considered signal peaks and the program scales each of these data points according to the optimal value indicated by the user. In the on-line version, values of 50, 100, 200, 400 or 800 are available. The average data signal is divided by the optimal value to give a scaling factor, and the point being normalised is then scaled by dividing it by this factor.SeqDoC algorithm to remove blank trace at the end of each electropherogram by deleting the terminal values from the traces where all channel values are less than 50. In both options, the reference trace guides the search for the starting point in the target trace by limiting the region that is being scanned for peaks to 200 data points ahead of the analogous starting position determined for the reference trace. This solution improves removal of the blank trace at the beginning of the target trace as target traces may often display noise peaks ahead of the real data and these peaks may interfere with proper assignment of the data starting point.If use of the 25-plex system option is indicated by the user, the program scans the channels for peaks that may correspond to this system and trims the beginning of the trace accordingly. Otherwise, it uses the first data peak of the first channel as the data starting point and uses the SeqDoC algorithm except for one minor modification: the sequences are sampled every three data points and difference scores are calculated for the subsequent 15 data points. If the difference score is reduced by the insertion or deletion of a single data point, then the target traces are adjusted accordingly, by either duplication or removal of the data point at the test position.The alignment procedure follows that of the SeqDoC for the four genotyping channels. Briefly: following normalisation and alignment of the sequences, a 'difference profile' is calculated. For each data point, values of the target trace are subtracted from the equivalent values in the reference trace and the changes are highlighted by squaring the difference value and multiplying the result by the square root of the sum of the differences of other channels which change in the opposite direction. The three images are generated by the Perl GD::Graph module, and are returned to the user in web page format. The difference trace displays bidirectional peaks at the points of base changes between the reference and target traces. SNPmplexViewer also adds an orange graph for the fifth channel to the electropherogram of the reference trace, indicating the size standards.To help the user verify the alignment, the output includes three aligned images: the reference, target and difference-profile electropherograms Figure . The algBased on the original target file, a modified trace file is generated. Using the Bio::Trace::ABIF module, the data of the four channels of the target trace are replaced with the normalised and aligned data-point values. The raw data from the fifth channel of the reference trace is used to overwrite the fifth channel of the modified target trace file. This operation is ignored if no fifth dye is present in the input traces. For the ABI310 sequencer model, the trace matrix is overwritten by a matrix that indicates that the channels now represent pure dyes.SNaPshot . To better handle such traces, at least in the 25-plex traceability system [The reference trace provides SeqDoC algorithm. Data points are added or removed from the target trace in order to align the target peaks with their corresponding reference peaks. Unlike sequence traces, peaks in SnaPshot traces are dense and routinely overlap, hence, minor modifications to the SeqDoC algorithm were required to achieve a refined alignment: the traces were sampled every three data points, not five, and difference scores were calculated for the subsequent 15 data points and not 30. Following the alignment step, the target peaks are located in positions that agree with the positions of the size-standard peaks in the fifth channel of the reference trace. The transfer of this fifth channel data into the fifth channel of the target file creates a modified target file that is very similar to what would have been obtained if the target fragments had been electrophoresed together with the size standards : Platform-independentProgramming language: Perl CGIOther requirements: Requires Perl GD::Graph and Bio::Trace::ABIF modulesLicense: None for web access, GNU GPL for source codeAny restrictions to use by non-academics: No restrictionsExample data set is available via the project home page.CGI, common gateway interface; SNP, single nucleotide polymorphismThe authors declare that they have no competing interests.SNPmplexViewer. All authors have read and approved the final manuscript.YS was responsible for the Perl programming. AS performed the ABI sequencer analyses. ES drafted the manuscript and designed SNPmplexViewersource code. Perl script in text format.Click here for file


The field of medical systems biology aims to advance understanding of molecular mechanisms that drive disease progression and to translate this knowledge into therapies to effectively treat diseases. A challenging task is the investigation of long-term effects of a  treatment, to establish its applicability and to identify potential side effects. We present a new modeling approach, called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT), to analyze the long-term effects of a pharmacological intervention. A concept of time-dependent evolution of model parameters is introduced to study the dynamics of molecular adaptations. The progression of these adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The trajectories provide insight in the affected underlying biological systems and identify the molecular events that should be studied in more detail to unravel the mechanistic basis of treatment outcome. Modulating effects caused by interactions with the proteome and transcriptome levels, which are often less well understood, can be captured by the time-dependent descriptions of the parameters. ADAPT was employed to identify metabolic adaptations induced upon pharmacological activation of the liver X receptor (LXR), a potential drug target to treat or prevent atherosclerosis. The trajectories were investigated to study the cascade of adaptations. This provided a counter-intuitive insight concerning the function of scavenger receptor class B1 (SR-B1), a receptor that facilitates the hepatic uptake of cholesterol. Although activation of LXR promotes cholesterol efflux and -excretion, our computational analysis showed that the hepatic capacity to clear cholesterol was reduced upon prolonged treatment. This prediction was confirmed experimentally by immunoblotting measurements of SR-B1 in hepatic membranes. Next to the identification of potential unwanted side effects, we demonstrate how ADAPT can be used to design new target interventions to prevent these. A driving ambition of medical systems biology is to advance our understanding of molecular processes that drive the progression of complex diseases such as Type 2 Diabetes and cardiovascular disease. This insight is essential to enable the development of therapies to effectively treat diseases. A challenging task is to investigate the long-term effects of a treatment, in order to establish its applicability and to identify potential side effects. As such, there is a growing need for novel approaches to support this research. Here, we present a new computational approach to identify treatment effects. We make use of a computational model of the biological system. The model is used to describe the experimental data obtained during different stages of the treatment. To incorporate the long-term/progressive adaptations in the system, induced by changes in gene and protein expression, the model is iteratively updated. The approach was employed to identify metabolic adaptations induced by a potential anti-atherosclerotic and anti-diabetic drug target. Our approach identifies the molecular events that should be studied in more detail to establish the mechanistic basis of treatment outcome. New biological insight was obtained concerning the metabolism of cholesterol, which was in turn experimentally validated. A central aim of medical systems biology is the development of computational models and techniques to study molecular mechanisms that drive disease progression In the present paper we propose a computational approach that overcomes the aforementioned issues. The approach, called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT), employs mathematical modeling to predict the long-term effects of a pharmacological intervention. We introduce a concept of time-dependent descriptions of model parameters to study the dynamics of molecular adaptations, making use of experimental data obtained during different stages of an intervention. These model parameters typically represent reaction rate constants (linked to mass action or Michaelis-Menten kinetics), but could be any other quantity expressible in a mathematical model. The progression of adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The obtained dynamic trajectories of model parameters, as well as metabolite concentrations and -fluxes, are constrained by the network topology and kinetic equations of the molecular processes. As such, our method exploits and integrates the merits from constrained-based modeling approaches  and kinetic modeling. ADAPT is therefore particularly useful to study biological systems from which the network topology is relatively well known, such as the mass fluxes in metabolic pathways. The modulating effects on these pathways via interactions with the proteome and transcriptome, which are less well understood, can be captured by the time-dependent descriptions of the parameters. Hence, as will be shown here, an advantage of ADAPT is that pathway adaptations can be described without the necessity to develop detailed kinetic models of the modulating mechanisms. Moreover, it could provide a means to capture the effects of complex phenomena such as cell differentiation, developmental changes, and aging, that may contribute to the progression of long-term adaptations. The approach originates from our previous work in which computational modeling was used to identify necessary differences in parameters to describe how one phenotype could be evolved from another Relevant applications of ADAPT are the investigation of metabolic pathways in relation to progressive diseases such as Type 2 Diabetes and cardiovascular disease. Dyslipidemia is an important risk factor for these diseases, and recognized markers such as plasma triglycerides, LDL- and HDL-cholesterol, are used in clinical settings to assess disease risk and status. However, the underlying molecular mechanisms inducing adaptations in lipid metabolism are not fully understood, complicating the development of effective treatments. In the present study, ADAPT was applied to a model of mouse hepatic lipid and plasma lipoprotein metabolism to identify which metabolic adaptations are induced upon pharmacological treatment of mice by liver X receptor (LXR) agonist T0901317. The family of liver X receptors, LXRα and LXRIn the following sections we present a step-by-step generic description of the methodology underlying ADAPT. The approach consists of several steps which are discussed below and schematically depicted in A detailed description of the experimental materials and procedures is available linear ordinary differential equations:In ADAPT the mathematical model is first used to describe the untreated phenotype (8)obtained , step 3.The calculated trajectories of molecular states, parameters and fluxes can be used for a wide range of analysis techniques to study the induced molecular adaptations. A class of computational techniques that is frequently applied to systems biology models  is sensitivity analysis In the present section ADAPT is applied to identify which metabolic adaptations are induced upon pharmacological treatment of mice with LXR agonist T0901317 up to three weeks. The perturbation by means of this treatment starts at the proteome level and subsequently induces adaptations at the other levels  were included. We also included data on VLDL production, VLDL clearance, VLDL particle size, and VLDL composition. Quantitative data on hepatic cholesterol uptake in untreated mice was derived from An extensive data set of C57BL/6J mice treated with T0901317 for 0, 1, 2, 4, 7, 14, and 21 days was generated and included in the computational analysis. A detailed description of the experimental materials and procedures is available . The modulating and regulatory effects on metabolic pathways via interactions with the proteome and transcriptome levels are less well understood. At present it is not yet feasible to include a full mechanistic description of these interactions in the model. Note that the computational model does not include any mathematical descriptions of processes involved in LXR activation and its transcriptional response. ADAPT overcomes the problem of undermodeling by introducing time-dependent parameters that account for the missing interactions.The rationale for including the aforementioned biological processes in the mathematical model is to generate a close and balanced match between model complexity and the available experimental data. The level of detail at which certain biological processes can be integrated in a mathematical model is determined by the selection of molecular species, as well as the type and quality of the measurements. Therefore, the model size and complexity of the reaction equations was kept to a minimum. Furthermore, model development was focused on integrating biological pathways from which the topology is well known and a substantial amount of components were measured quantitatively, i.e. mass fluxes at the metabolome level. The network topology of metabolic pathways is relatively well known and is available for different organisms in various pathway databases such as listed in Pathguide  measurements from pooled mice plasma showed an increased level of HDL-C  and different timescales. Therefore, in many cases insufficient information is available to define the essential interaction mechanisms which are modulated by a specific treatment intervention. Hence including a full mechanistic description of these interactions in a mathematical model is not possible. ADAPT provides a solution to overcome the problem of undermodeling by introducing time-dependent parameters that account for the missing interactions. We have previously reported a concept of varying parameters A concept of time-varying parameters is also used in linear parametric-varying (LPV) control analysis. LPV systems are predominantly applied to design gain-scheduled multivariate controllers To allow for time-dependent optimization of model parameters according to To account for variations in the dynamic behavior as well as experimental and biological uncertainties, a collection of smoothing splines was calculated using a Monte Carlo approach in which random samples of the experimental data were generated. This provides the possibility to determine the propagation of data uncertainty through model predictions, and hence allows to distinguish between predictions that are well constrained and as such can be made confidently, and those that display a large variation in possible outcomes. In case when parameter trajectories (or trajectories of states and fluxes) are not well-constrained by the experimental data, it might be worthwhile to study relative differences of these trajectories compared to the untreated phenotype, which for several cases display consistent behavior Click here for additional data file.Text S2An example of the multi parametric sensitivity analysis (MPSA) method is demonstrated and compared with a local sensitivity analysis method.(PDF)Click here for additional data file.Text S3Description of the mathematical model of hepatic lipid and plasma lipoprotein metabolism, including an overview of the states, parameters, fluxes, and ordinary differential equations.(PDF)Click here for additional data file.Text S4An overview of the experimental data and corresponding spline interpolants that were used as input for ADAPT.(PDF)Click here for additional data file.Text S5Analysis of the influence of changing the number of time steps used in ADAPT on the model outputs and regularization error.(PDF)Click here for additional data file.Text S6Analysis of the influence of changing the regularization strength on the model outputs and regularization error.(PDF)Click here for additional data file.Text S7Analysis of unwanted side effects when targeting (PDF)Click here for additional data file.Text S8Analysis of relative adaptations of trajectories to identify consistent model behavior.(PDF)Click here for additional data file.Text S9Analysis of the identifiability of parameters for the untreated phenotype, using ADAPT and the Profile Likelihood method.(PDF)Click here for additional data file.Text S10Comparison of model outputs obtained with time-dependent parameters used in ADAPT and an analysis using time-constant parameters.(PDF)Click here for additional data file.


Previous research addressed the development of a classification scheme for quality improvement systems in European hospitals. In this study we explore associations between the 'maturity' of the hospitals' quality improvement system and clinical outcomes.The maturity classification scheme was developed based on survey results from 389 hospitals in eight European countries. We matched the hospitals from the Spanish sample  with those hospitals participating in a nation-wide, voluntary hospital performance initiative. We then compared sample distributions and explored associations between the 'maturity' of the hospitals' quality improvement system and a range of composite outcomes measures, such as adjusted hospital-wide mortality, -readmission, -complication and -length of stay indices. Statistical analysis includes bivariate correlations for parametrically and non-parametrically distributed data, multiple robust regression models and bootstrapping techniques to obtain confidence-intervals for the correlation and regression estimates.Overall, 43 hospitals were included. Compared to the original sample of 113, this sample was characterized by a higher representation of university hospitals. Maturity of the quality improvement system was similar, although the matched sample showed less variability. Analysis of associations between the quality improvement system and hospital-wide outcomes suggests significant correlations for the indicator adjusted hospital complications, borderline significance for adjusted hospital readmissions and non-significance for the adjusted hospital mortality and length of stay indicators. These results are confirmed by the bootstrap estimates of the robust regression model after adjusting for hospital characteristics.We assessed associations between hospitals' quality improvement systems and clinical outcomes. From this data it seems that having a more developed quality improvement system is associated with lower rates of adjusted hospital complications. A number of methodological and logistic hurdles remain to link hospital quality improvement systems to outcomes. Further research should aim at identifying the latent dimensions of quality improvement systems that predict quality and safety outcomes. Such research would add pertinent knowledge regarding the implementation of organizational strategies related with quality of care outcomes. Since his landmark publication in 1966, numerous studies have addressed Avedis Donabedian's theory to understand health care quality in terms of structure, process and outcomes . SubstanMore recently, calls have been made to bring back to the attention of the quality of care debate the 'forgotten dimension of structure', which includes, for example, the role of senior leadership, organizational management, incentive structures and information management . While sWhile hospitals' investment in quality systems in terms of professional time, documentation systems and management are substantial, the evidence base of the impact of these systems at the level of clinical practice or patient safety is not well developed and research on this topic has only recently been gaining interest -11. As pThis cross-sectional study was performed in Spanish hospitals that participated in the MARQuIS study and at the same time contributed data to participate in a national, voluntary benchmarking initiative from IASIST, 20 Top Hospitals.http://qshc.bmj.com/content/18/Suppl_1An EU-funded research project to assess the effectiveness of quality improvement strategies in European hospitals in order to provide information on contracting requirements for patients moving across borders and for individual hospitals when reviewing the design of their quality strategies. The project ran from 2005 to 2009 and was led by the Avedis Donabedian University Institute, Autonomous University of Barcelona, Spain. Overall, 389 hospitals were recruited from eight countries  to participate in the data collection. As part of the project a maturity classification model for hospital quality improvement systems was developed. The results of the project can be accessed free-of-charge in a supplement to Quality & Safety in Health Care: http://www.iasist.es/20 Top Hospitals is a voluntary, benchmarking initiative to which all Spanish hospitals can subscribe. In its tenth year since inception, this initiative provides information to top management on hospital level indicators and in six specific areas: nervous system, respiratory diseases, heart diseases, trauma, orthopedics and obstetrics. In 2009, 155 hospitals, representing about half of all Spanish hospitals in the National Health Service participate in this initiative. IASIST methodologies draw on a yearly database of more than 3 million discharges obtaining case-mix and risk adjusted clinical and non-clinical performance measures in order to inform clinical quality management and to provide top-management with information on hospital performance. More information can be found at: The MARQuIS questionnaire was administered in 2006 by online-survey to 113 hospitals in Spain, 105 of which provided data to compute the maturity index on the development of the quality improvement system. Of these hospitals, 51 were also involved in the IASIST project in 2007 . Permission was sought from hospitals to merge the databases from the MARQUIS and IASIST project in order to pursue the objective identified above. The final sample comprised 43 hospitals, where sufficient information and permission were provided for the study to take place.Outcome variables were obtained from the IASIST data and include quality and patient safety indicators at hospital level which are calculated on the basis of the administrative hospital discharge data set Minimum Basic Data Set (MBDS), a compulsory data set for all hospital admissions in Spain. We included four hospital-level indicators which are calculated based on the ratio of the observed to expected number of cases, taking into consideration hospital-specific case mix and severity Table . DetailsFor the main independent (predictor) variable, we used the MARQUIS Maturity Index (MI) of the hospitals' quality improvement system, which is based on a self-evaluation of the quality system by the hospital quality manager. The maturity index was developed as part of a larger study ,13. The "Which of the following quality improvement activities take place in your hospital": . Answers are scored on a four-point scale: "1 = Yes, this activity takes places systematically in most departments (> 50%), 2 = Yes, this activity takes place in most departments (> 50%), but not systematically, 3 = Yes, this activity takes place in some departments (< 50%), 4 = No, this activity does not take place".Responses to the items were weighted according to the plan-do-check-act (PDCA) cycle: items reflecting preparation or planning of a quality strategy received a lower weight than items reflecting that a strategy has been implemented, which in turn received a lower weight than an item reflecting that data on the implementation of a strategy was available and used to guide quality improvement efforts. Based on the mean scores of each of the dimensions, an aggregate score for the maturity of the hospitals' quality improvement system was computed. Previous research addressed the evaluation of the psychometric properties and validity in a larger sample of European hospitals and indicated good construct validity and concordance between self-assessment and expert review . MoreoveU-test.In a first step, we described measures of central tendency and dispersion for hospital characteristics and IASIST quality and patient safety indicators and compared hospital characteristics in the sample with those in the Spanish MARQuIS sample using Fisher's exact test and the Mann-Whitney In a second step, we explored associations between dependent and independent variables. Distributions of all variables were tested using the Kolmogorov-Smirnov statistic. Analysis of correlations between the maturity index and the dependent variables were performed using appropriate parametric (Pearson's product moment correlation coefficient) and non-parametric methods (Spearman's correlation coefficient) . ConsideIn a third and final step, multiple regression analysis was performed to assess the effect of MI after adjusting for hospital structural characteristics that might confound the associations. We used a multiple regression model with hierarchical variable entry to separately assess the effect of MI and the remaining structural characteristics of the hospital  on the dependent variables. We created dummy variables to introduce the categorical variables to the multiple regression models . In order to obtain normality or homocedasticity (constant variance) of dependent variables we used the Box-Cox transformation, estimating where appropriate the constant using the maximum verisimilitude function in the R statistical software . ConsideOf the 113 hospitals participating in the Spanish MARQuIS and the 51 of these that participated in the IASIST project, 43 provided sufficient information on both data sets and agreed for the data sets to be merged  cycle; that is, it is not a simple yes-or-no assessment of compliance with each of the items. In Table The indicators presented in Table The means differ slightly from 1 since the data reflects a sub-sample of the IASIST data that was used to calculate the indicators. We checked the distributions of data and performed tests of normality using the Kolmogorov-Smirnov test. We identified various hospitals with outliers for the four indicators . Since these outliers are not data entry errors but rather reflect real life variations in the indicators we retained them in further analysis. Nevertheless, we adjusted our data analysis strategy considering that high leverage on the predictor variables can have an influential effect on the correlation and regression coefficients.R, we drew 1000 repeated samples of equal size (with replacement) of our data and plotted the distribution of the newly derived data  and the bootstrapping technique. Using the bootstrapping procedure in Figure Figure Figure Figure The scatter plots  reflect the data from the bootstrap analysis and indicate a negative relationship between adjusted hospital mortality and adjusted hospital length of stay and MI on the one hand and a positive relationship between adjusted hospital complications and readmissions and MI on the other hand. Given that an MI score of 1 reflects the best possible quality improvement system, the data suggest that hospitals with the best MI have the lowest complications and readmissions, but also the highest mortality and length of stay, although this fails to show statistical significance. The plots above displayed various outliers, reflecting different hospitals for each of the variables analyzed.In a final step, in order to adjust for the effect of potential confounding factors for associations between MI and the dependent variables, we entered the following variables into a multiple regression model: hospital type, ownership, and size. To this end, we created dummy variables for hospital type (university vs non-university), ownership (public vs private) and for size . We used hierarchical variable entry with step 1 comprising the variable MI, and adding the dummy variables for hospital type, ownership and size in step 2. Where indicated, Box-Cox transformations were performed to obtain normality of the dependent variables. Because of the effect that outliers have on the coefficients in the regression analysis when using the ordinary least squares method we applied a regression model that, by reducing the weight of outlying cases, produces robust estimates. Two estimation methods were used (Huber and bi-weighting) and compared to the estimates of the ordinary least square regression model. We then chose the model with the lowest standard error of residuals  to quality and patient safety outcomes in 43 Spanish hospitals. The data suggests that adjusted hospital complications are associated with the development of the hospitals' quality improvement systems: hospitals with more mature quality improvement systems present lower complication rates. Similar results, although borderline significant and partly confounded by hospital size can be observed for adjusted hospital readmissions. Perhaps contrary to common expectations we found the associations between MI and the dependent variables of mortality to be negative, meaning that hospitals with a more mature quality improvement system had higher mortality rates than other hospitals. Even though this association was not significant it raises issues about the use of the MI to identify hospitals with high mortality. Adjusted hospital length of stay was not significantly associated with MI, probably because of insufficient variability in the data set with regard to this variable.The link between MI and hospital adjusted complications was corroborated systematically in our analysis and was irrespective of level of analysis (bi-variate correlation vs. multivariate regression) or estimation method . Hospital complication indicators have been computed based on administrative data for more than 15 years and risk-adjustments have matured substantially over this period . The assThe link between MI and readmission is much weaker in our data and at best borderline significant. It seems that it is more difficult to establish a relation between the few items related to discharge planning in the MI and the readmission indicator. Difficulties in discriminating between hospital readmission rates based on their discharge procedures were also demonstrated by recent research in the USA , even thMortality rates have for a long-time been discussed as to whether they are a valid measure of hospital performance. Since the introduction of the standardized hospital mortality rates and subsequent refinements of the methods for its calculation ,27, adjuFor adjusted hospital length of stay we did not detect any relationship with MI. It is possible that the variability of the data is too limited to establish any associations. On the other hand, the link between maturity of the quality improvement systems and length of stay is less explored and the direction of the associations, if it existed, is unclear. Length of stay, while a common indicator in assessing hospital performance , is pronThe research presented here has some limitations which are discussed below. The sample size of this study is limited. From the initial 51 hospitals that participated in both the MARQuIS study and IASIST, we obtained consent from only 43 hospitals to match the data from both. We observed some outliers which we retained in the analysis; firstly because there was no conclusive reason to exclude these hospitals since the data do not appear to be erroneous, and secondly, in order not to reduce the sample size further. To account for the effect of the outliers and to obtain more robust estimates for the correlation coefficients we applied the bootstrapping procedure to resample hospitals from our database. In addition, we used robust estimation methods for the regression analysis to ensure that the coefficients are not inflated by the outliers or observations with high leverage. Although we did adjust the multiple regression analysis for a number of potential confounding factors such as type, ownership and size of the hospital, we were not able to adjust for additional factors known to be related to hospital outcomes, like nurse-patient ratios or organizational culture -33. MoreDespite the limitations outlined above, we believe that the results presented here raise some important questions for further research on exploring the impact of quality improvement systems on patient level outcomes. This is particularly so since our results are in line with previous research that demonstrates some effects of quality improvement systems on hospital compliance with quality criteria or on patient level outcomes, even though this effect is not always systematic  nor its In order to advance the research on quality improvement we propose the following. Firstly, research should target the development of a valid, reliable and feasible instrument to assess the quality improvement system. The maturity index used in this study or comparable measures being developed elsewhere ,35-37 prIn our analysis of 43 Spanish hospitals we found the maturity of the hospitals' quality improvement to be consistently associated with adjusted hospital complications and to some extent with adjusted hospital readmissions, while adjusted hospital mortality and adjusted hospital length of stay were not found to be associated. From this data it seems that hospitals with more mature quality improvement system have lower rates of complications. A number of methodological and logistic hurdles remain to link hospital quality improvement systems to outcomes. Further research should address these and the quality improvement agenda should continue to include research on the effectiveness of quality improvement to demonstrate and justify its value.The authors declare that they have no competing interests.OG and RS conceived of the study. MS and MC collaborated in the design of the study. OG, NM and AT performed the statistical analysis. OG drafted the manuscript. All authors critically read, revised and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/11/344/prepubDefinition of hospital adjusted complications. List of complications included in the calculation of the indicator 'hospital adjusted complications'.Click here for fileStatistical specifications. Statistical specification and links to the R syntax for the bootstrapping procedure, robust regression procedure and Box-Cox transformation procedure.Click here for file


The interaction of the association of dopamine genes, impulsivity and childhood trauma with substance abuse remains unclear.To clarify the impacts and the interactions of the Catechol -O-methyltransferase (COMT) gene, impulsivity and childhood trauma on the age of onset of heroin use among heroin dependent patients in China.202 male and 248 female inpatients who meet DSM-IV criteria of heroin dependence were enrolled. Impulsivity and childhood trauma were measured using BIS-11  and ETISR-SF (Early Trauma Inventory Self Report-Short Form). The single nucleotide polymorphism (SNP) rs737866 on the COMT gene-which has previously been associated with heroin abuse, was genotyped using a DNA sequence detection system. Structural equations model was used to assess the interaction paths between these factors and the age of onset of heroin use.Chi-square test indicated the individuals with TT allele have earlier age of onset of heroin use than those with CT or CC allele. In the correlation analysis, the severity of childhood trauma was positively correlated to impulsive score, but both of them were negatively related to the age of onset of heroin use. In structure equation model, both the COMT gene and childhood trauma had impacts on the age of onset of heroin use directly or via impulsive personality.Our findings indicated that the COMT gene, impulsive personality traits and childhood trauma experience were interacted to impact the age of onset of heroin use, which play a critical role in the development of heroin dependence. The impact of environmental factor was greater than the COMT gene in the development of heroin dependence. Substance abuse is a complex disease, which is the result of an interaction among various factors, such as genetic and environmental risk factors, and the personality of the individuals Other studies have focused on the impulsive personality of substance abusers and Moeller et al have found that the most marked difference in characteristics between substance users and control subjects is a high level of impulsivity in substance users There are much evidence to show that impulsivity is related to the underlying biological and psychosocial processes. For example, linkage and association studies have indicated that polymorphisms in COMT, Dopamine D4 receptor and Dopamine Transporter (DAT) gene are associated with abnormally aggressive behavior or personality disorders Given the current evidence, we predict that the interaction of COMT gene, childhood trauma and impulsive personality contribute to the development of heroin abuse. Indeed, there are few studies which looked at the relationship among these factors. The respective influence of COMT gene, childhood trauma and impulsivity personality in the development of heroin dependence, is also unknown. In the study, we try to explore how the COMT gene, severity of childhood trauma and impulsive personality interacted to influence the age of onset of heroin use and the respective degree of impact for the three factors.This research protocol was approved by the Ethic Committee of the Shanghai Mental Health Center. Subjects were recruited by the research team who were trained to provide confidentiality. According to the related law in China, the patients in drug rehabilitation center have the equal civil rights of citizens except they are required to accept the drug rehabilitation for a certain period of time according the severity of their drug dependence. In order to minimize the possible coercion, the researchers interacted with the potential participants individually without the police officer present, and explain to the potential subjects that their participation will not result any consequences or benefits to them while in the rehabilitation center, and they may decline to participate or withdraw from the study at any time. Only after it was certainly that the participant understood and agreed, the informed consent was signed. The study participants were given a copy of the consent form and the status of inform consent was not needed to notify the policy office in the rehabilitation center.A total of 450 currently abstinent patients who had previously been dependent on heroin were recruited from drug rehabilitation centers in Shanghai, a large metropolitan city in Eastern China, which included 202 men and 248 women with a mean (SD) age of 33.9 (7.7) years. A diagnosis of heroin dependence according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) was made during a psychiatric interview using structured clinical interview for DSM-IV (SCID-I). Subjects with other axis I psychiatric diagnoses were excluded from the study. A self-completion form developed for the study which included basic demographic information and the history of drug use  was collected. Urine and blood samples were obtained and screened for illicit drugs and genotype.1. The Barratt Impulsiveness Scale (BIS-11). The BIS-11 is a 30 item self-report scale that is structured to assess aspects of impulsivity relating to rash behavior or thoughts by asking the respondents to answer questions about the ways they act and think without reference to any specific period 2. Early Trauma Inventory Self Report-Short Form (ETISR-SF). The ETISR-SF evaluates trauma across four subscales, including general trauma, physical abuse, emotional abuse, and sexual abuse. Higher scores in each domain indicate more traumatic experiences Genomic DNA for each subject was extracted from lymphocytes according to a modified phenol/chloroform method. The COMT gene SNP rs737866 was genotyped on the ABI Prism 7900 sequence detection system. For quality control, 5% of random DNA samples were genotyped twice for each SNP to calculate genotyping error. The genotyping accuracy was 100%.Independent t- tests were used to compare the mean NS subscale scores and the mean onset age of heroin use among groups defined according to different COMT gene SNPs genotypes. Structural equations model is a generalization of linear regression and factor analysis models; it is used for the multivariate analysis. We used a model , where a value smaller than 0.1 is considered as a good fit and very good when it is smaller than 0.05, Bentler and Bonnet’s NFI (normed fit index) where a value over 0.95 is considered as a good fit and Akaike’s information criterion (AIC). AIC adopts a balanced approach taking into account the quality of the model, and it discourages “over-fitting”. Statistical power was computed with a null assumption of good fit corresponding to an RMSEA of 0.05 and an alternative assumption of bad fit for an RMSEA equal to or higher than 0.1. A power higher than 90% was obtained. All the tests were performed with a = 5%. For statistical analyses, SPSS15.0 and Amos 7.0 software were used for the structural equations models.Demographic and drug use history of the subjects is summarized in Based on our previous study, the COMT rs737866 SNP genotype was re-coded as a dichotomous variable for the recessive model . The individuals with TT allele have earlier age of onset of heroin use than whom with CT or CC allele . Pearson correlation analysis showed the BIS total score was negatively related to the age of onset of heroin use.The ETISR-SF score was positively correlated to impulsive score , but negatively correlated to the age of onset of heroin use.As showed in All coefficients, except Attentional and Nonplanning, were significant in the model, varying from 0.11 to 0.30. The coefficients associated with the latent variable CT were statistically significant, ranging from 0.38 to 0.83, were in favor of its robustness.As showed in In this model, the direct influence of the COMT gene SNP on the age of onset of drug use was significant (−0.14), that is, individuals with TT genotype were more likely begin drug use earlier. There was also moderately significant indirect influence of the COMT gene SNP rs737866 through impulsive personality (0.01). In turn, motor impulsive personality was also influenced by COMT gene SNP rs737866; The coefficient was 0.11.This study highlights several points:Firstly, we observed that differences in the severity of childhood trauma and the variation of the COMT gene SNP rs737866 was associated with the development of drug use. Although both of these factors influenced the age of onset of drug use, childhood trauma appeared to be the more influential than the COMT gene.et a.l suggested that COMT may interact with childhood trauma to impact the risk of developing schizotypal personality traits et al. showed that the COMT gene modulates the influence of childhood abuse on anger traits, which was associated with eating disorder and borderline personality disorder et al. indicated that the COMT gene modulated the association of serious life events and impulsive aggression in female patients with borderline personality disorder Gene and environmental interactions on psychiatric diseases have been widely accepted and our study suggests that there is an interaction between the COMT gene and childhood trauma in the development of heroin use. This is in line with previous literature that both of these risk factors may interact to contribute to the occurrence of psychiatric disease. Specifically, Savitz Secondly, impulsive personality was also found to play a role in the onset of heroin use. Both childhood trauma and the COMT gene were associated with the age of onset of drug use through impulsive personality. Among the three factors, the motor factor appeared to have the largest effect on the age of onset of drug use.According to the theory, which stipulates that personality is influenced by environment and genes, we observed that impulsive personality was significant correlated to childhood trauma and the COMT gene. Among the three factors, motor factor had the strongest influence on the onset of drug use. Motor factor was defined as “acting on the spur of the moment” by Patton There are some limitations need to be considered. Our structural equation model only partially took into consideration of a limited number of variables, so we could not rule out other possible factors which may influence the age of onset of drug use. Moreover, our model only focused one behavior phenotype, that is the age of onset of drug use, we could not be sure that the interaction have underline other behavior phenotype, which may also influence the development of drug use disorders. Now we are investigating if the result is reproducible for other behavior phenotype.Despite these limitations, the study provides new insights into the complex relationship between biological and environmental factors in the development of mental disorders. Moreover, the structural equation model is a neat way to explore the respective influence of different factors in the development of complex disorders. In this study, childhood trauma is the main risk factor underlying heroin use while environmental and genetic factors took into account.


For instance, two neuronal membrane models with identical populations of ion channels, one written with conductance-based currents, the other with drift-diffusion currents, undergo transitions into and out of repetitive oscillations through different mechanisms and for different levels of stimulation. These differences in excitability are observed in response to excitatory synaptic input, and across different levels of ion channel expression. In general, the electrophysiological profiles of membranes modeled with drift-diffusion and conductance-based models having identical ion channel populations are different, potentially causing the input-output and computational properties of networks constructed with these models to be different as well. The drift-diffusion formulation is thus proposed as a theoretical improvement over conductance-based models that may lead to more accurate predictions and interpretations of experimental data at the single cell and network levels.Electrical signaling allows communication within and between different tissues and is necessary for the survival of multicellular organisms. The ionic transport that underlies transmembrane currents in cells is mediated by transporters and channels. Fast ionic transport through channels is typically modeled with a conductance-based formulation that describes current in terms of electrical drift without diffusion. In contrast, currents written in terms of drift and diffusion are not as widely used in the literature in spite of being more realistic and capable of displaying experimentally observable phenomena that conductance-based models cannot reproduce (e.g. rectification). The two formulations are mathematically related: conductance-based currents are linear approximations of drift-diffusion currents. However, conductance-based models of membrane potential are not first-order approximations of drift-diffusion models. Bifurcation analysis and numerical simulations show that the two approaches predict  However, ionic transport through channels is driven by electrical drift, as assumed in CB models, but also by diffusion Electrical signaling allows fast transfer of information within and between cells. Electrical signals are produced by ionic transport within tissues, and in particular, across the membranes of cells. Most transmembrane ionic transport is mediated by membrane-spanning proteins that may either mechanically translocate ions across the membrane (transporters), or facilitate ionic diffusion by forming pores drift-diffusion (DD) and models of membrane potential constructed using CB, or alternatively, using DD currents will be referred to as CB or DD models. CB models are generally regarded as good descriptions of membrane potential, have been studied extensively linear approximation of its DD counterpart around the reversal potential of the current , a time-dependent function representing external stimulation, or a time- and voltage-dependent function representing synaptic input. The smallest time-dependent stimulation will consist of 5 epochs: (1) bottom, with stimulus amplitude 0 nA, (2) ramp up (Up), (3) constant stimulation with amplitude equal to the maximum reached by the ramp (Top), (4) ramp down (Down), and (5) bottom again. This stimulation protocol will be referred to as up-top-down (UTD). Unless otherwise specified, Up, Top, and Down epochs have the same duration with Up or Top will be called recruitment current, and the stimulus amplitude during Down at which a transition between sustained oscillations and rest occurs will be called de-recruitment current.The Synaptic input. The activity of the presynaptic cells is simulated by generating spike trains with gamma-distributed interspike intervals, each with a mean rate mes  see . The timRecall that the CB approximation to the DD current is valid only for voltages near the reversal potential of the current. Therefore, for any given potential, the DD model (Eq. 5) cannot be written as a CB model by simply replacing the DD currents by their CB approximations. However, it is still possible to investigate whether the dynamics from the DD model are qualitatively different to those of the CB model by rewriting Eq. 5 so that the relative contributions of the channels to the change in membrane potential are the same. To do so, start by multiplying the right hand side of the DD model by a normalization current relative expression of ion channels. For instance, if all the currents in the DD model are divided by the maximum amplitude of the Narelative expression of KThe normalization term for the DD model could be Shab gene in central neurons of adult Drosophilae.g. Kv2.1 para/DmNav gene also present in adult Drosophilae.g. Nav1.1–1.9 in vertebrates The channel kinetics used here are based on the biophysical properties of delayed-rectifier KThe normalizing amplitude The comparisons between DD and CB models shown subsequently are made assuming steady state currents are obtained after excluding spirals, which means the membrane potential oscillates when the system is near a focus point. In contrast, the membrane potential does not oscillate when the saddle node points initially move toward the saddle-node and eventually diverge from it, which means that if cycles of the system represent sustained oscillations in the membrane potential. Limit cycles are asymptotically stable attractors. This means that the membrane potential will go into sustained oscillations if the bistable if it has two attractors (e.g. a fixed point and a limit cycle).The resting potential . Asymptotically stable focus points are such that the membrane potential oscillates toward the resting value. In contrast, the membrane potential converges to asymptotically stable node points monotonically (without oscillating). Recall that a bifurcation occurs when either the number, the type, or the stability of the fixed points or cycles of the system change The system in Eqs. 5–6 has at least one asymptotically stable attractor for parameters within the physiologically meaningful range. In other words, there should be either a fixed point or a limit cycle (sustained oscillation) that the system goes back to. If all the fixed points are unstable, there is no resting membrane potential and sustained oscillations are expected to occur  bifurcations occur when a focus point changes in stability. Subcritical and supercritical AH bifurcations are associated to bistable and monostable systems, respectively. Systems like Eqs. 5–6 that undergo a subcritical AH bifurcation, typically loose or gain an unstable limit cycle. In the former case, the membrane is typically bistable, with a limit cycle and a stable fixed point separated by an unstable cycle. At the bifurcation the unstable cycle closes into the fixed point and dissappears, leaving an unstable focus and the limit cycle around it. Prior to the bifurcation the system could go to rest or into repetitive spiking depending on its initial conditions. After the bifurcation, the system is monostable and its only asymptotic behavior is the sustained oscillation (repetitive spiking). In Saddle-node (SN) bifurcations the number of fixed points changes between 3 and 1 (or viceversa) and are associated with non-monotonic fold limit-cycle (FLC) bifurcation is such that two cycles appear or disappear (similar to SN). One cycle is unstable and surrounds the stable fixed point. The other cycle is stable and surrounds the unstable cycle. The unstable cycle delimits the basins of attraction of the fixed point and the limit cycle. FLC bifurcations occur near subcritical Andronov-Hopf bifurcations.The values used in the simulations presented here can be found in http://www.python.org).Numerical simulations shown in this manuscript were performed using the solver integrate.odeint (lsoda from the FORTRAN library odepack) available from the Python module scipy  as ncreases  and the tial see  and S5. The choice of First, note that gating causes the divergence between the steady state B model  . This tirent see .Qualitatively different behaviours can be observed in the CB and DD models as a function of DD model , but itsDD model . In contDD model , but bisDD model . Both morecruitment current in these conditions may be smaller than the slow passage through Hopf). Further, the mechanisms by which repetitive spiking starts when Top stimulus slightly larger than the recruitment current in the CB model (Top amplitude = 705 pA). In this case, repetitive spiking starts after a relatively long delay in the CB model. The reason is that the ramp allows both variables of the system to change, thereby moving the system toward one of its attractors. At the start of the ramp, the system moves toward its nearest stable fixed point, which is a focus. Shortly before the Top amplitude is reached, the fixed point undergoes an AH bifurcation in which the focus becomes unstable, leaving a limit cycle as the only attractor of the system  with respect to the recruitment and de-recruitment current amplitudes, and (2) with respect to the recruitment and de-recruitment firing rates. As a rule of thumb for slow ramps, the recruitment current is more likely to be smaller than the de-recruitment current with a larger recruitment firing rate larger in comparison to the de-recruitment rate .The whole-membrane behavior of the DD model shares many of the properties of CB models in that it contains parameters that can be found experimentally and displays dynamics observable in excitable cells. As shown in previous paragraphs, the two models are mathematically related: the CB formulation for current is a linear approximation of the DD formulation around the reversal potential for the current. In agreement with previous reports The DD and CB models are compared by setting the relative contributions of the channels to be identical in both models. One of the most obvious differences between the DD and CB models is their range for repetitive oscillations. In general, if the ratio between the maximum Kslow passage through HopfInterestingly, both models display two different hysteresis-related behaviors with ramping inputs. In very general terms, the recruitment current is larger than the de-recruitment current if the steepness of the ramps is shallow. In contrast, the recruitment current is smaller than the de-recruitment current when the ramps are very steep. For particular interest, the two models display what has been reported as a Note the only persistent current in the model is the delayed rectifier Kents see , thus hiDrosophila data. The choice of the specific channels and animal model is based on the large availability of whole-cell patch clamp recordings, which provide measurements that can be directly used as model parameters. In addition, the use of Drosophila data allows interpretations in terms of specific channel genes. Since the parameters used here are representative of central neurons in Drosophila the post-synaptic currents are assumed to be mediated by fast cholinergic receptors The models are constructed based on For theoretical interest, writing the model in terms of ratios using the normalizing amplitude The analysis and fitting procedures presented here can guide studies geared toward understanding cellular responses recorded in the laboratory under genetic and pharmacological manipulations. In this respect, it is worth remarking that this is one of only a few modeling efforts that incorporates data from an identified neuron in a single model system in which both the ion channel genes and their biophysics are known. Importantly, the results shown here in regard to the differences between the DD and CB models also hold if the models are adjusted to match the cellular dynamics in other model organisms. For instance, if the parameters are adjusted so that the membrane has the input resistance and capacitance measured from a vertebrate cell of interest, with fast-transient Naelectrophysiological profile of a cell The construction of the membrane models in this article rests on the hypothesis that the relative presence of channels in the membrane determines, to a large extent, what we could refer to as the The results presented here highlight the importance of exploring the different responses produced by two kinds of extensions of the DD models presented here: spatially detailed models and networks. These two extensions (and others) are likely to yield very different results and predictions in comparison to those from existing CB models, potentially prompting a reevaluation and possibly, a re-interpretation of accepted theories originated from network models of nervous function.Figure S1Fitting of voltage clamp data from Shab channels . The data (black dots) were digitized from the original publication and fitting was done with a python script. The blue curves are fits to the data parameters (TIF)Click here for additional data file.Figure S2Cholinergic Synaptic input.(TIF)Click here for additional data file.Figure S3Steady state currents for the DD and CB models for . The top curve in each panel of corresponds to (TIF)Click here for additional data file.Figure S4Trajectories of membrane potential with different levels of depolarization from rest . DD (solid) and CB (dashed) for a) b) (TIF)Click here for additional data file.Figure S5Profile of responses to square pulses of different amplitude. The pulses lasted 200 milliseconds with (TIF)Click here for additional data file.Figure S6Comparison of phase trajectories and instantaneous firing rate during UTD stimulation. Panels a and c show, respectively, DD and CB trajectories in the phase plane Top. b and d Graphs of c and f Instantaneous firing rates as a function of time. The horizontal lines illustrate the recruitment and de-recruitment firing rates .(TIF)Click here for additional data file.Text S1Overview of the derivation of the expressions for current driven by electrodiffusive transport and notes about bifurcation analysis.(TEX)Click here for additional data file.


By improving lipid standardization, the Centers for Disease Control and Prevention's (CDC's) Lipid Standardization Program and Cholesterol Reference Method Laboratory Network have contributed to the marked reduction in heart disease deaths since 1980. The objective of this study was to estimate the benefits  and costs attributable to these lipid standardization programs.We developed a logic model that shows how the inputs and activities of the lipid standardization programs produce short- and medium-term outcomes that in turn lead to improvements in rates of cardiovascular disease and death. To calculate improvements in long-term outcomes, we applied previous estimates of the change in heart disease deaths between 1980 and 2000 that was attributable to statin treatment and to the reduction in total cholesterol during the period. Experts estimated the share of cholesterol reduction that could be attributed to lipid standardization. We applied alternative assumptions about the value of a life-year saved to estimate the value of life-years saved attributable to the programs.Assuming that 5% of the cholesterol-related benefits were attributable to the programs and a $113,000 value per life-year, the annual benefit attributable to the programs was $7.6 billion. With more conservative assumptions , the benefit attributable to the programs was $338 million. In 2007, the CDC lipid standardization programs cost $1.7 million.Our estimates suggest that the benefits of CDC's lipid standardization programs greatly exceed their costs. Cholesterol awareness and control are important factors in reducing deaths from heart disease in the United States and are a key focus of health promotion and clinical practice . Age-adjAn important but sometimes overlooked contribution to improvements in cholesterol awareness and control has been provided by the Centers for Disease Control and Prevention's (CDC's) Lipid Standardization Program (LSP) and Cholesterol Reference Method Laboratory Network (CRMLN) ("lipid standardization programs" hereafter). The LSP is an accuracy-based program that defines benchmark reference methods and maintains stable pools of reference testing materials ,7. Its sAs a cost-saving measure, in 2008 the National Heart, Lung, and Blood Institute (NHLBI) retracted its 50-year budgetary commitment to the LSP. Without the NHLBI funding, the LSP and CRMLN programs may not be able to continue in their present form. In response, the Cardiovascular Biomarker Standardization Steering Committee of the National Association of Chronic Disease Directors (NACDD) asked NACDD to conduct a cost-benefit study of the LSP. The objective of this study was to estimate the benefits and costs of the LSP and CRMLN. Results of the study may be used by policy makers to determine the value of lipid standardization.We first developed a logic model for assessing the impact of the CDC lipid standardization programs . Logic mThe programs are currently supported by CDC and previously were jointly funded by NHLBI. We do not consider participation costs incurred by clinical laboratories, manufacturers, and research funding agents because participation in the programs is voluntary.The fundamental activities of the programs are to define reference methods and maintain reference materials for total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides. CDC uses a standardized testing protocol on well-characterized and uniform serum materials to eliminate potential reference bias and allow program participant results to be compared directly to the CDC reference results. The programs maintain a set of frozen serum pools that exhibit a wide range of lipid concentrations. Long-term maintenance of these pools is essential to ensure that reference values of these samples do not drift over time.Using the reference measurement procedures and materials, the LSP conducts standardization of clinical laboratories involved in epidemiologic and clinical research. Establishing a long-term, accuracy-based reference allows results to be compared across different laboratories and over time, which is necessary when conducting multicenter clinical trials; comparing lipid measurement values to past values, such as the baseline period in clinical and epidemiologic research; or comparing lipid measurement values across clinical and epidemiologic research studies. Recommended accuracy goals for lipids and lipoprotein tests, which include both bias and imprecision, have been developed by the National Cholesterol Education Program -13. ThesThe CRMLN uses the standardized reference methods and materials to certify a network of reference laboratories that seek to replicate the accuracy of the CDC laboratory. In turn, this network of laboratories certifies participating manufacturers and clinical testing laboratories. By calibrating manufacturers' equipment and testing methods against the CDC reference standards, the CRMLN increases the accuracy of all tests conducted using these supplies, even for clinical laboratories that do not participate in the program. This system allows for accurate comparison of test results to the clinical practice guidelines established by the Adult Treatment Panel III .The programs' inputs, activities, and participants are intended to produce the following short-term outcomes:Standardized epidemiologic results, which lead to improved treatment recommendations.Standardized clinical trials, which lead to improved comparisons of alternative treatments.Standardized routine testing methods, which lead to the improved cholesterol test accuracy necessary for improved diagnosis.The short-term outcomes — in combination with nonprogram factors — lead to the following medium-term outcomes for patients and health care providers: increased cholesterol awareness, better practice patterns, improved diet and exercise, better drugs, and improved diagnosis of patients with high cholesterol.The medium-term outcomes combine to produce better cholesterol control, which improves patient health outcomes in the long term by reducing medical events and lowering rates of cardiovascular disease and death. The true benefits of the lipid standardization programs arise from improvements in these long-term patient outcomes.It is difficult to quantify precisely the effect of the lipid standardization programs on these short-, medium-, and long-term outcomes. For most of the outcomes, standardization of lipid measures is a prerequisite that can support and promote — but does not by itself guarantee — improved public health outcomes. For example, standardization supports clinical trials of new cholesterol-lowering therapies, but development of new therapies also depends on research and development efforts, technologic breakthroughs, and careful clinical testing.Because we cannot directly attribute outcome changes to program operations, our approach is to investigate improvements in each short- and medium-term outcome during recent years. We discuss qualitatively how the standardization efforts of CDC programs may have facilitated the improvement, but we do not attempt to estimate precisely the share of each improvement that is attributable to the programs. We also do not place a dollar value on the benefits of improvements in short- and medium-term outcomes because the true benefits to patients are associated with improvements in long-term outcomes.To determine the improvements in long-term outcomes, we applied previous estimates of the change in heart disease deaths between 1980 and 2000 that was attributable to treatment with statins and the overall reduction in total cholesterol during the period. Ford et al  and CapeWe considered alternative assumptions about the share of deaths prevented or postponed and life-years saved that were attributable to the lipid standardization programs. We asked experts to estimate the percentage of lipid reduction during the period that was attributable to the lipid standardization programs. The 4 experts work on cardiovascular disease in various settings . The experts were asked to base their estimates on information on the reduction in CHD deaths between 1980 and 2000 and the estimate  is sometimes used to assess the cost-effectiveness of interventions, but the conceptual basis for this benchmark is debatable . A recenWe calculated the dollar benefits of the life-years gained attributable to the lipid standardization programs under alternative assumptions about the life-years attributable to the LSP and CRMLN and the value of a life-year gained. We then compared the dollar benefits of the programs to the cost of the LSP and CRMLN in fiscal year 2008, $1.7 million.As detailed in the Appendix, we found suggestive evidence linking the LSP and CRMLN to improved short- and medium-term outcomes. Program data indicate that laboratories participating in the programs achieve high levels of accuracy. This accuracy supports standardized epidemiologic results, standardized clinical trials, and standardized testing methods used by clinical laboratories, which can lead to improved test accuracy.Assuming the median expert panel estimate of 5% of the cholesterol-related benefits attributable to the programs and a value per life-year of $113,000, the benefits attributable to the programs are estimated to be $7.6 billion . AssuminWe conducted a sensitivity analysis to examine how the estimated benefits change under alternative assumptions about the percentage attributable to the LSP and CRMLN  in compaOur estimates suggest that the benefits of CDC's lipid standardization programs greatly exceed their costs. Deaths from heart disease fell dramatically between 1980 and 2000 , and thiOur analysis has several potential limitations. First, our estimates could implicitly overestimate the value of the improvements in life expectancy stemming from reductions in heart disease deaths; however, this does not appear to be the case. Improvements in heart disease deaths are clearly documented in national life expectancy and cause-of-death data. Less evidence exists on the value of this improvement, but a study by Murphy and Topel  providesSecond, our analysis could attribute too much of the gain in life-years resulting from reductions in heart disease deaths to cholesterol-related factors. We relied on studies by Ford et al  and CapeThird, our estimate depends on the share of lipid reduction that is attributable to the lipid standardization programs, and this parameter was not precisely measured. Although the parameter was based on the opinion of experts familiar with the programs, these experts noted that the parameter was difficult to estimate. Even when we included more conservative parameters, the benefits attributable to the programs were still sizeable. Because this is probably the most important potential limitation of our study, it is worth considering additional alternative estimates. The sensitivity analysis shows how the benefits change with the percentage attributable to the LSP and CRMLN for values ranging from 0.001% to 5%. Even when the LSP and CRMLN are responsible for a smaller share of the reduction in life-years attributable to cholesterol-related factors than our expert panel estimated, the benefits of the programs substantially exceed their costs.Fourth, our estimate of the benefits attributable to the programs is based on the estimated effects of the programs on cholesterol between 1980 and 2000. It is not clear whether standardization would suffer today and in the future if the LSP and CRMLN ceased to exist. One might expect that test values would drift away from true values over time, but it seems unlikely that bias levels would return to their 1980 levels. This limitation can be assessed using Fifth, our estimates of the improvements in heart disease deaths that are attributable to cholesterol-related factors are based on studies for the period between 1980 and 2000. If more recent data were available, the number of life-years saved by cholesterol-related factors would likely increase because heart disease death rates have continued to fall since 2000.Sixth, assigning a dollar value to life-years saved is controversial, and when a value is assigned, debate remains about the actual value to set. Nevertheless, the estimated benefit from the lipid standardization programs remains high even if life-years are valued at $50,000 per year.Finally, our analysis does not include health care cost offsets or increases associated with the reduction in heart disease deaths. In principle, it might be possible to model spending on heart disease for individual patients; however, the level of modeling necessary is beyond the scope of this study.This is the first study attempting to quantify the benefits of CDC's lipid standardization programs. At least 1 study has assessed the benefits of improving the accuracy of other clinical tests. Gallaher et al  estimateAs noted in the introduction, the NHLBI discontinued its funding of the LSP in 2008, raising questions about the program's future. Our estimates provide evidence of the benefits and costs of the CDC lipid standardization programs that may help policy makers decide whether to continue funding the programs.


Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data. Since the invention of the computer, people have acknowledged the possible advantages that computers might offer in clinical decision making. In 1960, Ledley and Lusted It is the goal of this work to combine the advantages of non-linear modeling using advanced models with the easy applicability and interpretability of score systems Consider a problem where The method that we present here involves three modifications with respect to the above model. The first two modifications involve the type of transformation A problem with the proposed method is that it can still lead to a solution including small intervals, which are often irrelevant for clinicians. In order to reduce the number of small intervals, equation (2) is iteratively reweighted as follows  The area under the receiver operating curve (AUC) was used as a model selection criterion with 10-fold cross-validation. For estimation of the link function (risk prediction), the 10-fold cross-validated likelihood using a clinical kernel 2adj is reported. For the first illustration, clinicians were interested in the model's performance when using a cut-off on the estimated risk. Sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR−) and the diagnostic odds ratio (DOR) The performance of the model was expressed in terms of discrimination and calibration In order to visualize the resulting models for use in clinical practice, one needs to get an idea on who will use the model and in which circumstances. A first class of users will be clinicians, who may want to use it in an examination room, at the bedside of the patient, or during a consultation. The clinician might therefore opt for a computer-based application or an application on a portable device such as a smartphone. A second class of users might be the patients. Depending on the problem at hand, the model might be created from variables that the patient can calculate at home. Using a paper-based version, the patient will be able to check the evolution of his risk during treatment. A paper-based version might also be helpful for the clinician when explaining the results to a patient. Additionally, one might opt for a table or figure representation. Different possible implementations are discussed and illustrated below. Since the outcome of an ICS model consists of variable intervals and their corresponding weights, the model can be implemented in a user-friendly way. As illustrated in The proposed method is illustrated on two diagnostic problems. For the first illustration, we used a dataset of 3,511 patients with an adnexal mass recruited within the framework of a large multi-center study using standardized examination techniques and definitions 2adj equaled 0.73 on the training and 0.72 on the test set. The model underestimated the risk of malignancy on the test set  In a second study, we compared the ICS approach with the classical score system approach, i.e. score systems obtained by approximating a logistic regression model see . In the This paper proposes a new methodology in order to bridge the gap between advanced mathematical diagnostic models and their applicability in clinical practice. This gap is the consequence of increasingly complex models which can no longer be interpreted nor applied by clinicians. Where others proposed to approximate advanced models by means of score systems or rules in a post-processing step, our approach starts from a model representation which is interpretable and usable in a clinical setting. By building an additive model with constant effects within intervals, the result can be represented as a questionnaire or score chart. However, as opposed to other score system approaches, the ICS enables better control of possible information loss since the number, position, and length of the intervals are automatically optimized by the model.The ICS model presented here can be used in daily clinical practice to solve a broad range of clinical questions. The class of binary classification problems can be solved with the current model equations of the ICS methodology. The areas of application include, but are not restricted to, diagnosing tumor malignancy, detecting abnormalities in biomedical signals, estimating the risk of cardiovascular disease, diabetes, pregnancy failure, tumor recurrence within 5 years after surgery, estimating the therapeutic effect of different therapies, and detecting predictive factors for various conditions. Thanks to the different implementation possibilities, the model and the resulting risk estimate or diagnosis can be presented to the patient in a more understandable way than any existing method. The improved interpretability in combination with good model performance enables a better doctor-patient communication which is becoming increasingly important in the age of informed patient decision making. In particular, the color-based representation can be suitable for this task. Patients will easily understand that the addition of a variable value within a red area increases while a value within a blue area decreases their risk.In order for the ICS methodology to become a widely applicable model, extensions to model architectures for problem settings other than binary classification are needed. Firstly, the additive model structure can be expanded such that problems with more complex covariate effects can be modeled as well. The current equations can be extended to an ANOVA structure, where interactions between different covariates can be further included. Where the current model is able to include interactions that are known from the literature, an ANOVA extension will make it possible to include additional interactions.A second extension involves clinical classification problems with more than two outcomes (multi-class problems). Although clinical multi-class problems are most often reduced to binary classification problems, only multi-class models can give the most appropriate answer to the problem. Adnexal tumors are often categorized as being benign or malignant, whereas four or more categories could be considered, such as benign, borderline, primary invasive and metastatic A third extension of the ICS methodology relates to disease prognosis. Survival studies are very common in clinical research. A few examples are the prognosis of primary operable breast cancer patients after surgery, the prognosis after organ transplantations, the prognosis of severely burnt patients and the prognosis of patients after a stroke. By incorporating the ICS methodology in flexible models for the analysis of survival data A last challenge concerns the increasing number of high-dimensional data, such as genomics and proteomics data. The current ICS model equations are able to deal with clinical datasets, containing a large number of patients and a moderate number of covariates. In order to apply this model to high-dimensional data, specialized optimization algorithms will need to be developed. Further research on the ICS methodology together with preprocessing and feature selection algorithms for high-dimensional data, are needed for this purpose.Supporting Information S1Detailed description of the data, Table S1 , Table S2 (Description of the variables of the pregnancy data set considered in this work.), Illustration of the use of a nomogram for ovarian cancer diagnosis, Fig. S1 (Nomogram for the diagnosis of ovarian cancer derived from a logistic regression model), Illustration of a model implementation created with the ICS methodology, Software.(PDF)Click here for additional data file.Movie S1Software implementation of the table-based representation of the ICS model for diagnosing adnexal masses.(MOV)Click here for additional data file.Movie S2Software implementation of the figure-based representation of the ICS model for diagnosing adnexal masses, without color indication.(MOV)Click here for additional data file.Movie S3Software implementation of the figure-based representation of the ICS model for diagnosing adnexal masses, with color indications.(MOV)Click here for additional data file.


Meanwhile, many kinetic rates are unknown, making global system analysis intractable in practice. In this article, we demonstrate a computational pipeline to help solve this problem, using the exocytotic process as an example. Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles that contain a wide range of intracellular molecules. During this process a set of proteins called SNAREs acts as an engine in this vesicle-membrane fusion, by forming four-helical bundle complex between (membrane) target-specific and vesicle-specific SNAREs. As expected, the regulatory network for exocytosis is very complex. Based on the current understanding of the protein-protein interaction network related to exocytosis, we mathematically formulated the whole system, by the ordinary differential equations (ODE). We then applied a mathematical approach  to estimating the kinetic parameters in the fundamental subsystem (without regulation) from limited in vitro experimental data, which fit well with the reports by the conventional assay. These estimates allowed us to conduct an efficient stability analysis under a specified parameter space for the exocytotic process with or without regulation. Finally, we discuss the potential of this approach to explain experimental observations and to make testable hypotheses for further experimentation.One difficulty in conducting biologically meaningful dynamic analysis at the systems biology level is that  Exocytosis is the fundamental physiological process that leads the traffic of vesicles to fuse with the plasma membrane, releasing its vesicle contents into targeted cells that control many cellular processes in vivo of the system is complex. As many kinetic rates are unknown, and concentrations of proteins, complexes and substrates keep changing in both vivo and vitro environments, a biologically meaningful, global system analysis is intractable in practice. Earlier, Mezer et al. Although experimental studies have provided invaluable insights for the underlying exocytosis mechanisms, the process of exocytosis is a typical example to show the difficulty in conducting an analysis at the systems biology level in vitro experimental data. Third, based on the above estimates, we can therefore approximately study the stability behavior of this system with and without MUNC18 regulation. We then attempt to explain experimental observations about different fusion efficiency caused by the change of SNARE proteins’ concentration and multiple complexes in the SNARE-induced membrane fusion. Moreover, we make a few interesting predictions that can be verified by further experimentations.In this paper, we utilize the exocytotic process as a model system to present a computational framework for system modeling and analysis. Similar to From the view of gene network, the exocytotic process is a sophisticated combination of sequential interactions of well-defined proteins and protein complexes The well-known foundations -dependent regulation.where CaS stands for the complex of synaptotagmin and one Cawhere the generic notation Tsc represents the protein complex of tSNARE and synaptotagmin binding four CaMUNC18 is an important regulatory protein for the exocytotic system Putting together, we have formulated a mathematical model by the ordinary differential equations (ODE) to capture how the concentrations of different proteins and complexes vary with time and how they interact each other. Based on the law of mass action and Michaelis-Menten Kinetics, and using the conventional notation One may raise the question, due to the complexity of this network, whether we have empirical evidence enough to show the concept we try to put forward. In a recent article, we in vivo concentration of CaThe dynamics for We notice that self-association of syntaxin is possible such that One can show that the ODE system of Eq.(1) complies with the detailed balance principle and the mass conservation. For instance, because the only products of the reactions involving MUNC18 are Smc and FHCDenote all of the variables (concentrations) in Eqs.(1)–(5) by a vector in vivo of the system can be very complex. In addition, most paremeters remain unknown in this ODE system, including the initial concentrations of different proteins and complexes, and the reaction rates in both vivo and vitro environments. Hence, it is almost intractable in practice to carry out a global system analysis. As a first step to overcome this difficulty, we attempt to estimate the rate parameters for the basic steps of exocytotic process. Among different methods, we choose the technique of inverse problem that has two advantages: the required data size is small, and the algorithm guarantees the uniqueness and efficiency The whole system for the exocytotic process as described in Eq.(1) is a typical example to show the general difficulty in systems biology As the well-known machinery, chemical reactions.Using the inverse problem technique [30] to estimate rate parameters requires initial concentrations. In the following the symbol In the experimentation, researches use the fluorescence intensity, To recover the appropriate reaction rates, we apply technique introduced by Estimation of rate parameters of the subsystem Eq.(10), as summarized in vivo, tSNARE is already preformed in the plasmic membrane; and then carried by vesicles, vSNARE (VAMP2 in our case) binds with it to generate fusion. In this sense, we assume Our analysis has shown that the final steady state level of the fusion is highly dependent on initial concentrations. Obviously, three proteins  must exist at To be concise, we define Case-A assumes that the initial concentration of SNAP25 and syntaxin are the same such that Case-B studies the problem without the assumption of same initial concentration of SNAP25 and syntaxin. Our stability analysis shows that, provided Case-C considers a more general case that the reaction ratio vivo, SNAP25 and Syntaxins may have been already preincubation (preformed) into tSNAREs on the plasmic membrane, before vSNARE proteins (VAMP2 in our case) approach, as carried by vesicles.Since we are mostly interested in the final steady state-level of fusion, i.e., in vitro experimental data (the embedded panel) Our analysis explains why the outcome of fusion process depends on the way to put these three proteins into the system We furthermore study the stability behavior of the system involving the regulatory protein MUNC18. As discussed above, the MUNC18-dependent regulation has two types: . Assume the binding rate  are virtually zero, which mean all of these proteins exist in the form of FHC and/or FHC**. The second steady-state is the low fusion level  is equivalent to the optimization problem of.The rational of the inverse problem is based on the following theorem: Suppose the solution of Eq.(10) Given initial condition Gradient representation: using forward difference to approximate Applying steepest descent to approach the global minimum starting with some initial guess,Using adjoint scheme to approximate Hessian Using the approximate solution given by step 2 as initial guess , and using Quasi-Newton method with The formal claim from the stabilizing analysis of the fundamental subsystem Eq.(10) is as follows: Define Assume the initial concentrations of SNAP25 and syntaxin are the same so that If Consider the case of if if if if A more general case is that the reaction ratios  and then evaluate the two eigenvalues, denoted by For steady state In the same manner, we have shown the results presented in case-B and case-C.For the system described in Eq.(17), there are three steady states, which are.if Proof: The proof is similar to the case of system Eq.(10) without regulation.