Diagnostic discordance for osteoporosis is the observation that the T-score of an individual patient varies from one key measurement site to another, falling into two different diagnostic categories identified by the World Health Organization (WHO) classification system. This study was conducted to evaluate the presence and risk factors for this phenomenon in a large sample of Iranian population.Demographic data, anthropometric measurements, and risk factors for osteoporosis were derived from a database on 4229 patients referred to a community-based outpatient osteoporosis testing center from 2000 to 2003. Dual-energy X-ray absorptiometry (DXA) was performed on L1–L4 lumbar spine and total hip for all cases. Minor discordance was defined as present when the difference between two sites was no more than one WHO diagnostic class. Major discordance was present when one site is osteoporotic and the other is normal. Subjects with incomplete data were excluded.In 4188 participants , major discordance, minor discordance, and concordance of T-scores were seen in 2.7%, 38.9% and 58.3%, respectively. In multivariate logistic regression analysis, older age, menopause, obesity, and belated menopause were recognized as risk factors and hormone replacement therapy as a protective factor against T-score discordance.The high prevalence of T-score discordance may lead to problems in interpretation of the densitometry results for some patients. This phenomenon should be regarded as a real and prevalent finding and physicians should develop a particular strategy approaching to these patients. Osteoporosis is defined as a systemic skeletal disease characterized by low bone mass and micro-architectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture ,2. This 'T score' is a statistical definition which indicates the difference between patient's BMD and mean bone density of normal population in the age of 20 – 30 (reference population) [Discordance in diagnosis of osteoporosis is defined as presence of different categories of T scores  in two skeletal sites of an individual patient . This phActually, one of the reasons for measuring BMD in several sites is the presence of discordance, which can affect the diagnosis and therapeutic plan in an individual person. Various studies have analyzed the prevalence and impact of T-score discordance on different aspects of management of osteoporosis -9. HowevGiven this background and concerning the need for the estimation of the impact of this phenomenon in our country, we aimed to evaluate the presence and risk factors for T-score discordance in a large sample of Iranian population.Participants in this study were 4229 persons who underwent bone densitometry in outpatient clinic of Endocrinology & Metabolism Research Center in Tehran from 2000 to 2003. A considerable proportion of these cases were healthy post-menopausal women referred by clinicians for densitometric evaluations. All study participants signed the informed consent for any scientific approach to their medical registered data. Our Institutional Review Board approved this study.A standardized questionnaire was filled before densitometry for all participants. Demographic data (including age and sex) as well as other known or suspicious risk factors for osteoporosis (including menopause, age at menopause, age at menarche, history of osteoporotic fractures, drugs, and smoking) were collected. All participants had their standing height measured using a stadiometer to the nearest 0.5 cm. Weight was measured on a standard weighting scale with a precision of 0.5 kg. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. All the BMD measurements were done for diagnostic purposes and none of the participants were on the treatment with bone active agents (hormone replacement therapy was not considered a bone active agent).BMD was measured at the lumbar spine and total hip with dual X-ray absorptiometry (DXA) using a Lunar DPXMD densitometer (Lunar 7164, GE, Madison, WI) by a trained operator according to the manufacturer's instruction. The instrument was calibrated weekly by using appropriate phantoms. Precision error for BMD measurements was 1–1.5% in the lumbar and 2–3% in the femoral regions. The device normative data of US population for spine BMD and NHANES III study for femur BMD were used as reference values.P values less than 0.05 were taken to indicate statistical significance. Statistical analyses were performed using Stata Statistical Package, version 8.0 (Stata Corporation, College Station, Tx).All the data gained from densitometry and questionnaires were entered into a comprehensive relational database. The participants with incomplete data were excluded from the study. To compare presence of various risk factors in participants with and without T-score discordance, chi-square test and independent sample t-test were used firstly. Potential risk factors were entered to a multivariate binary logistic regression analysis and the resulted odds ratios with 95% confidence intervals were reported. In sum, 4188 persons were enrolled in the study. Characteristics of all participants are summarized in Table Totally, 518 participants were diagnosed in osteoporotic range in hip area and 1036 participants in the lumbar area. T-score classifications are presented in Table T-score discordance was more prevalent in women than men (42.2% versus 36.5%, P = 0.042). The mean age of participants with discordance (54.8 years) was higher than the other group (52.5 years, P < 0.001). In 3848 female participants, the number of post-menopausal women with diagnostic discordances (951 of 2027) was significantly higher than pre-menopausal participants with discordance . In multivariate analysis  would show T-score discordance between hip and spine sites. Most of these discordances (38.9%) are from minor category, presenting difference on only one class, and could be due to minor variation in BMD techniques or some minor physiologic dissimilarity. Minor discordance generally does not influence the overall prognosis of patients; however, in the case of patients with one site normal and the other osteopenic, follow up of patients with hip osteopenia seems reasonable [The multivariate analysis we have implemented to the data could aid clinicians and diagnosticians to approach patients with different characteristics. According to our results, BMD measurement in both sites is necessary at least for older patients and post-menopausal women especially those with delay in menopause. Hormone replacement therapy, however, could decrease the diagnostic discordance and patients receiving estrogen and progesterone are more likely to have similar results in DXA scans of lumbar and femoral areas. This could be the result of drug effects on the BMD of lumber area .Generally, five different causes have been proposed for occurrence of discordance . PhysiolMajor discordance was observed in 2.7% of our participants, which is in agreement with the results of similar studies. In both major and minor discordances, lower BMD for lumbar spine was more prevalent. This could be due to several reasons. The difference between velocities of bone loss in different parts of human body could be the main reason . TrabecuIn 30% of our participants, the lumbar T-score was higher than hip T-score and this culminated in poorer hip diagnoses in 9.2% of participants. This phenomenon could be regarded as 'inverse discordance' and several factors may be involved in its occurrence. One of these reasons is the prevalent vitamin D deficiency in our participants. A recent nationwide study with random sampling from five major cities in Iran reported a high prevalence (about 80%) for vitamin D deficiency in Iranian population . Other sThe observation of 'inverse discordance' could not be regarded as a direct influence of more significant bone loss in femoral region. A known phenomenon named 'birth cohort effect' can play a role . This inThis study, as every other cross-sectional study, has a number of limitations. We could not rule out the possibility of referral bias for this study. As the study was performed in a referral center affiliated to a teaching hospital, the assumption of similarity of study population to exact community is not reasonable and we could not generalize the results to the Iranian population. The other limitation is the choice of multivariate analysis used in this study. With the current analysis, prediction of the presence or absence of T-score discordances is possible. However, prediction of the situation of one site according to results of the other site or choosing one site to measure BMD need further evaluations and analyses which was behind the scope of this study. Future studies using more powerful statistical analyses with larger sample sizes are needed to establish these imperative questions.The importance of existing discordance on the prognosis and fracture risk of patients needs further prognostic studies with long follow-up designs. The high prevalence of T-score discordance could induce some problems for the physicians in decision-making regarding these patients. In general, high prevalence of discordance in this study and similar studies suggests some defects in the cut-off values for definition of osteoporosis and osteopenia proposed with the WHO . To elimIn summary, this study indicates that about 40% of participants evaluated for bone density changes in a referral center may show diagnostic discordance, majority of them from minor class. This phenomenon should be regarded as a real and prevalent finding and physicians should become familiar with this topic. Clinicians should look for possible cause or causes of this occurrence and develop a particular strategy approaching to these patients.This study was supported by a grant from Endocrinology & Metabolism Research Center of Tehran University of Medical Sciences.In advance, suggestion of the design of the study was from AS. Data extraction and initial analysis were done by NKT and AH. AM performed additional analyses and wrote the first draft of the paper. AS and BL both had helpful and valuable comments in revising the paper. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Coalescent theory is a general framework to model genetic variation in a population. Specifically, it allows inference about population parameters from sampled DNA sequences. However, most currently employed variants of coalescent theory only consider very simple demographic scenarios of population size changes, such as exponential growth.Here we develop a coalescent approach that allows Bayesian non-parametric estimation of the demographic history using genealogies reconstructed from sampled DNA sequences. In this framework inference and model selection is done using reversible jump Markov chain Monte Carlo (MCMC). This method is computationally efficient and overcomes the limitations of related non-parametric approaches such as the skyline plot. We validate the approach using simulated data. Subsequently, we reanalyze HIV-1 sequence data from Central Africa and Hepatitis C virus (HCV) data from Egypt.The new method provides a Bayesian procedure for non-parametric estimation of the demographic history. By construction it additionally provides confidence limits and may be used jointly with other MCMC-based coalescent approaches. The coalescent is a very versatile stochastic model of the genetic variation in a set of sequences sampled from a population. It allows to accommodate a wide range of assumptions about rates and modes of evolution, and of population history -5.As the observed sequence data are positively correlated due to common ancestry, coalescent theory also provides a framework for understanding the relationship between a population's history and its genealogy. For instance, it has long been noted that genealogies of samples taken from exponentially growing populations tend to be "star-like" with short branch lengths near the root of the tree. In contrast, the inter-node distances in genealogies from constant-size populations typically are much more evenly spaced. Thus, coalescent theory quantifies the imprint that demographic development of a population leaves in the data. While the original theory was outlined for constant population size ,2, Slatkstatistically inferring the demographic history as a function of time from the sampled sequences [Therefore at least in principle the coalescent model provides a basis for equences ,8-12 or,equences -15. In pOnly recently methods have emerged that attempt the completely non-parametric estimation of the demographic function from the data. Polanski et al. proposed an approach based on pairwise distances , hence gThe advantage of the skyline plot over the method suggested by Polanski et al.  is that In this paper we propose a novel framework to non-parametric estimation of the demographic history. This approach relies on Bayesian reversible-jump MCMC inference  to obtaiThe remainder of the paper is organized as follows. In the next section we describe the mathematical and statistical theory of the new framework. Subsequently, we apply the method to simulated and biological sequence data and discuss the results. In the last section we briefly outline possible further extensions and related directions of research.Ne, where every individual has a single parent, the waiting time wn until any two of n sampled lineages coalesce is exponentially distributed with rate [n sequences there are therefore n - 1 intervals In, In-1,..., I2 with rates rn, rn-1,..., r2 and interval lengths wn, wn-1,..., w2. With  we denote the time until all samples have reached the most recent common ancestor.In a pan-mictic population with constant effective population size ith rate ,2. For nrn that varies in time t because of the change in the number of lineages. Thus, it is straightforward to also include variable population size in the coalescent simply by using the hazard rate . From standard theory in survival analysis [The coalescent model implies that the waiting time to the next coalescent event follows an inhomogeneous Poisson-process with a hazard rate analysis  it folloτi is the time at the beginning of the interval Ii. This is exactly the distribution from the variable population size coalescentwhere as developed in . The coawi are known Eq. 2 can be used directly to estimate Ne(t). This is typically done by maximizing the likelihood  assuming a simple parametric model for the population size change. For constant population size this has been done in [If the waiting times  done in , for mor done in .wi are marginalized out in favor of the actually observed data. In practice exact calculation of this sum is prohibitive, hence one relies on approximating MCMC methods [In a typical setting, however, the waiting times are themselves estimated from sequence data. In this case the total likelihood function will be a weighted sum of the likelihoods for all possible waiting times, so that in effect the  methods -10.wi in the inference of demographic parameters. However, this is justifiable if the phylogenetic error is much smaller than the error introduced by the coalescent. This will be the case if sequences are sufficiently long and the substitution rate is comparatively high .As a shortcut to avoid these computationally very expensive procedures one may also substitute the "true" waiting times by those obtained from inter-node distances of a single estimated gene tree (see, e.g.,  for an oNe(t) and allows population size changes only at the beginning and end of an interval Ii. The estimated effective population size  in interval Ii according to the skyline plot is given by the simple relationFor non-parametric estimation of population size, Pybus et al. suggested the "skyline plot" . This meThis is the maximum likelihood estimate under the assumed model of fixed change-points. The "generalized skyline plot" subsequently introduced by Strimmer and Pybus  reduces Ne(t) conditioned on observed or sampled inter-node distances wn, wn-1,..., w2 by determining the posterior distribution P(Ne(t)|wn, wn-1,..., w2). In order to sample the non-parametric demographic function from this posterior we use the reversible jump Markov chain Monte Carlo (rjMCMC) algorithm [t both a point estimate  – here we choose the posterior median – as well as the associated credible interval (e.g., the lower and upper 2.5% quantiles). If the considered inter-node distances wn, wn-1,..., w2 are fixed and obtained from a single estimated tree, the resulting method is already directly applicable to phylogenetically informative data such as viral sequences (this is the focus of this paper). However, sampling of non-parametric demographic functions can also be combined in a conceptually straightforward fashion with sampling of trees, as outlined below.In this paper we present a non-parametric approach to infer population size changes in time that overcomes the limitations of previous approaches. More specifically, we develop a non-parametric Bayesian estimator for the function lgorithm . As a rex consists of updating its prior distribution P(x) to a posterior distribution P(x|D) that takes account of the information in the observed data D. The relative evidence of the data for different values of x is summarized in the likelihood L = P(D|x) that accordingly plays a central role in the computation of the posterior via Bayes' theoremIn a nutshell, Bayesian inference of a parameter x is a high-dimensional vector. Instead, one utilizes computational procedures to efficiently draw random samples from the posterior. This in turn allows computation of summary statistics such as the median or the upper and lower 2.5% quantiles. Markov chain Monte Carlo (MCMC) is one particularly useful sampling algorithm as it doesn't require calculation of the sum  in the nominator of Eq. 4. Briefly, sampling via MCMC is done by constructing a Markov chain with the possible combinations of parameter values as "states", and the desired posterior as its stationary distribution. These properties can be guaranteed by following certain rules for accepting or rejecting proposed new parameter values. Here we use the Metropolis-Hastings-Green method, i.e. the reversible jump MCMC algorithm [x is the initial state, and  a proposed new state with proposal density , then the acceptance probability according to Green [For most realistic problems the posterior distribution cannot be computed analytically, in particular if lgorithm , that hato Green  is is the likelihood ratio P(D|)/P(D|x),  is the prior ratio P/P(x),  is the proposal ratio q/q(x), and  is the determinant of the Jacobian resulting from the potential change of dimension of the parameter vector.where Accordingly, for the application of MCMC to infer the functional form of demographic history a variety of components need to be specified:parameterization of the estimated function Ne(t)• a suitable likelihood function,• the prior distribution for each considered variable, and,• a Markov chain (i.e. acceptance probabilities).• rules to construct the In the following sections we now describe each of these elements in detail. For further general information on the statistical and mathematical background of the MCMC algorithm we refer to the many excellent monographs on this topic (e.g., ).Ne(t) by a piecewise linear function. This spline of first order degree consists of a first node at position a0 = 0 and height h0, followed by k internal supporting nodes at , ,..., , and a terminal node at  with height hk+1 Hence, the spline is defined for all t ∈ [0, T], and for any given k the it contains k free position parameters and k + 2 free height parameters. Note that, unlike in the skyline plot, we do not constrain the change-points a1,..., ak to lie on the grid points defined by the inter-node distances wi. Moreover, we also allow that the number of internal nodes k changes during sampling of the population function from the posterior. Hence, k is technically a hyper-parameter that controls the roughness of the resulting spline. As will be clear from the outline of the MCMC algorithm below, note that the final point estimate  obtained from posterior sampling will be a mixture of linear splines (i.e. a smooth and possibly nonlinear function) rather than a single spline.In our suggested procedure we approximate the sampled demographic history L employed in our procedure is the product of the densities of the waiting times between subsequent coalescence events, i.e. . This function depends via Eq. 2 on the effective population size Ne(t), and hence indirectly on the spline parameters ai, hi and k. Because Ne(t) is represented by a linear spline, calculation of the likelihood can be done in a computationally efficient fashion.The likelihood k, i.e.Following  we emploc is a normalizing constant to ensure that P(k) is a proper distribution. For the hard upper limit of the number of change-points we use kmax = 30. The parameter λ acts as a smoothing parameter, set in a typical analysis to about λ = 0.1 - 1.0.where λ we also suggest a hierarchical Bayes approach where λ is drawn from a Gamma distributionAs an alternative to using a fixed a and scale parameter b (for instance, a ≈ 0.5 and b ≈ 2 so that E(λ) = ab ≈ 1 and Var(λ) = ab2 ≈ 2).with some shape parameter a priori uniformly distributed in the interval [0, T]. As a simple trick to avoid very small inter-node distance we generate 2k + 1 random variables, and set the change-points aj = zj] [2for j = 1,..., k. The corresponding joint density isWe assume that the internal nodes of the spline are a0 = 0 and ak+1 = T.with hi we assume a Gamma distributionAs prior distributions for the heights hi|αi, βi)     (9)Gamma and β(t). This is particularly advisable if the population size is known in advance to be subject to large changes in time.which ensures that sampled heights are always positive. The parameters In a strict Bayesian approach, the choice of the prior distribution for the heights is completely external to the observed data. One simple possibility would, e.g., be to assume an arbitrary constant for the mean and variance. However, we recommend to follow a more pragmatic "empirical Bayes" route and to use the data at hand (or some other related data set) to obtain an informed guess about the prior heights. For example, an assumed constant population size as prior mean could be estimated using the method by Felsenstein . AnotherHowever, note that in practice the actual choice of prior height distribution seems to matter only little for estimating the posterior demographic function :There are four different possibilities to change the state defined by the parameters 1. varying the position of a change-point ,2. changing the height at a certain change-point,3. generating a new change-point ("birth" step), and4. deleting an existent change-point ("death" step).ηk, πk, bk, and dk the probabilities of the four moves given k, with ηk + πk + bk + dk = 1. In order to satisfy the requirement of detailed balance in the corresponding Markov chain the probabilities of birth and death steps (bk and dk) need to be synchronized [Let hronized . This caandc is chosen so that bk + dk < 0.9 for all k.where Next, we describe the individual procedures to propose and accept one of the above four moves as implemented in our program.hj is selected out of the k + 2 existing heights with probability . Second, a new height  is generated by  = hj exp(z), where z is a uniformly distributed random variable on . Third, the new height is accepted with probabilityFirst, a height α and β are from the prior distribution and  denotes the ratio of the likelihood of the new state  (with modified height) and the likelihood of the current state x.where aj is chosen randomly with probability . Second, its new position  within [aj-1, aj+1] is determined by drawing from the corresponding uniform distribution. Third,  is accepted with probabilityFirst, a change-point a* of the new change-point is found by uniformly drawing from (0, L), and let the neighboring nodes left and right of a* have positions aj and aj+1. Second, the corresponding new height h* is generated by randomly disturbing the current height Ne(a*) on the position a* according to Ne(a*) + zNe(a*) where z is a uniformly distributed random variable on the interval . Note that the birth step increases the dimension of the parameter vector from 2k + 2 to 2k + 4 as a new change-point and a new height are generated.First, the position The corresponding acceptance probability of the birth step is computed according to Eq. 5 with likelihood and prior ratios as above, and with proposal ratioand Jacobi determinanta* chosen from a1,..., ak with probability . Second, the corresponding height h* is also removed from the vector of spline parameters. The acceptance probability for the death step isThis is the inversion of the birth step and consists of removing a change-point. First, where the proposal ratio and the Jacobi determinant is the same as for the birth step.Ne(t) = c, where c is some rough estimate of population size, and k = 0. Second, 100,000 repeats of the MCMC algorithm are performed, of which the first 5,000 are ignored to allow for a "burn-in" period.In order to obtain an estimate of the effective population size in time we now proceed as follows. First, the Markov chain is started with an initial state that corresponds to a completely flat demographic function, i.e. ai, hi and k are obtained.Third, the remaining samples are thinned out by a factor of 1:50 to remove auto-correlation. As a result, 1900 independent samples from the joint posterior of the spline parameters  and associated confidence bands we compute the distribution of the effective population size at a number of fixed equidistant time points t1, t2,..., t1000 ∈ [0, T]. Finally, we report as summary statistics the corresponding median and the lower and upper 2.5% quantiles.Subsequently, in order to obtain a point estimate wn, wn-1,..., w2.)In this paper we have introduced non-parametric sampling of demographic histories assuming a fixed underlying genealogical tree  height distribution has to be set in advance (and independent of the T*). For the case of 0 <t <T* sampling of heights then proceeds as described above, while for T* <t <T – the region with no data from a given sampled tree – the heights are simply drawn from the respective prior distribution.Note that each sampled tree may have a different depth In order to test the potential of the proposed reversible jump MCMC algorithm we first applied it to synthetic data simulated according to various demographic scenarios. Subsequently, we reanalyzed two viral data sets from Central Africa and Egypt.The proposed framework has been implemented by us for the case of a single underlying genealogy. The program is written in the statistical computer language R  and is iTo install the APE package, simply run the R program, and enter at the R promptinstall.packages("ape")This downloads the APE package from the Internet. The proposed reversible jump MCMC approach is implemented in the function "mcmc.popsize" of which an extensive description along with examples can be obtained online by typinglibrary("ape")help(mcmc.popsize)into the R command window. The APE package also includes routines for plotting the inferred population function .Note that the use of this R program is only valid if the phylogenetic error is low – this is typically the case when the evolutionary rate is high and the available sequences are long . If the phylogenetic error is not negligible compared to the coalescent error, please use software such as BEAST .Ne(t) = 100) as well as exponential population growth (Ne(t) = l000et-), using 25 and 100 sampled lineages, respectively. To estimate the population size function we employed the proposed MCMC algorithm and the classic and generalized skyline plot. In the former the smoothing parameter λ was drawn from the hierarchical model with default parameters (a = 0.5 and b = 2).In the simulation setup we followed Pybus et al.  and StriNe(t) is a very good point estimator of the true demographic history. In addition, the 95% confidence band is also automatically obtained by the MCMC method. Interestingly, it can be immediately seen that the uncertainty in Ne(t) increases with a growing distance from the present. This simply reflects the fact that near the root of the tree for constant population size there are only few coalescent events.Figure In Figure In Figure Next, we applied our method to infer the demographic history from a set of HIV-1 sequences from Central Africa. These data was originally used by Vidal et al.  who examk = 9.25, i.e. the estimated effective degree of freedom is much less than that implicitly assumed in the classic skyline plot.Figure A comparison with the generalized skyline plot  is shownIn Egypt 10%-20% of the general population are infected with the Hepatitis C virus (HCV) . This enWe repeated their analysis with the reversible jump MCMC approach developed in this paper. We downloaded the sequence data from the HCV sequence database  and infeNe(t) in the log-plot changes). Near the present, the rate decreased again. Also note the broadening of the confidence interval since 1940 which reflects the sparsity of available observations. This implies that the claim of Tanaka et al. [Generally, the star-like shape of the inferred tree already is indicative of exponential growth. This is confirmed by both the skyline plot as well as by our analysis Figure . Moreovea et al.  that thea et al. .Ne(t). Unlike its predecessors, the classic and generalized skyline plots, it returns a smooth and realistic estimate of the demographic history and thus overcomes the constraints due to assuming a step function. Moreover, it automatically provides confidence limits. Nevertheless, the procedure is still computationally fast and can be run on any standard PC hardware.We have presented a new approach to non-parametric inference of demographic history from an inferred genealogy. This method is based on reversible jump MCMC sampling of the population size function Ne(t) are not taken into account.In our examples we demonstrated the advantage of non-parametric estimation of demographic history. Parametric estimation always assumes a certain functional form of population growth which may lead to problematic statements (cf. the HCV data set), in particular if the confidence bands of the estimated function λ) that controls the a priori degree of smoothing, and this is adjusted accordingly by the investigated data. In addition, a further advantage of our MCMC approach is that – in contrast to the skyline plot – at least in principle it is straightforward to incorporate it in more general MCMC sampling schemes that also take account of the uncertainty in the genealogy.From the methodological point of view, model selection via rjMCMC has the advantage that the effective dimension, i.e. the degree of smoothing, is automatically chosen in a data-driven manner. There is only one parameter (During the referee process we have learned that the authors of the software package BEAST  have devThis paper summarizes the main results from a master's thesis of R.O. supervised by K.S. and L.F. Accordingly, K.S. and L.F. jointly devised the project and R.O. carried out all analyses and simulations. All authors participated in the development of methodology. R.O. and K.S. wrote the manuscript. All authors approved of the final version.


Late-life depression is a common, chronic and recurring disorder for which guidelines recommend long-term therapy. The safety and efficacy of duloxetine for the treatment of major depressive disorder (MDD) were evaluated using data from elderly patients  who participated in a large, multinational, open-label study.17), the Beck Depression Inventory-II (BDI-II), the Patient Global Impression of Improvement (PGI-I) scale, and the Sheehan Disability Scale (SDS). Safety and tolerability were evaluated using discontinuation rates, spontaneously reported adverse events, and changes in vital signs, ECG, and laboratory analytes.Patients meeting DSM-IV criteria for MDD received duloxetine 80 mg/d (40 mg twice daily (BID)) to 120 mg/d (60 mg BID) for up to 52 weeks. Efficacy measures included the Clinical Global Impression of Severity (CGI-S) scale, the 17-item Hamilton Rating Scale for Depression . Significant improvement (p < .001) in both clinician- (CGI-S) and patient-rated (PGI-I) measures of improvement were observed at Week 1 and sustained throughout the study. Observed case response rates at Weeks 6, 28, and 52 were 62.9%, 84.9%, and 89.4%, respectively, while the corresponding rates of remission were 41.4%, 69.8%, and 72.3%. Adverse events led to discontinuation in 27 (26.7%) patients. Treatment-emergent adverse events reported by >10% of patients included dizziness, nausea, constipation, somnolence, insomnia, dry mouth, and diarrhea. Most events occurred early in the study. Mean changes at endpoint in blood pressure and body weight were less than 2.0 mm Hg, and -0.1 kg, respectively.Mean changes in HAMDIn this open-label study, duloxetine was effective, safe, and well tolerated in the long-term treatment of MDD in patients aged 65 and older. Late-life depression is a common and disabling condition which represents a substantial public health concern . The preDespite advances in available antidepressant treatments, limitations still exist in both efficacy and safety. Tricyclic antidepressants (TCAs) generally provide robust efficacy, but a number of side effects associated with this class of medications are of particular concern in older patients (e.g. anticholinergic adverse events, orthostatic hypotension, and sedation). Selective serotonin reuptake inhibitors (SSRIs) have provided an improved tolerability profile compared to the TCAs through lower rates of adverse events, and substantially lower toxicity in overdose . FurtherDuloxetine is a potent dual reuptake inhibitor of serotonin (5-HT) and norepinephrine (NE) . The effThe safety and tolerability of duloxetine have also been demonstrated under double-blind conditions. In placebo-controlled trials of duloxetine in patients aged 18 and older (doses from 40 – 120 mg/d) the most frequently reported adverse events were nausea, headache, dry mouth, fatigue, insomnia, and dizziness, while the overall safety profile of duloxetine was comparable to that of available SSRI medications . A compaHowever, these acute placebo-controlled trials of duloxetine were of 9 weeks duration or less. An NIH consensus panel has recommended that geriatric patients be given continuing antidepressant treatment for at least 6 months for a first episode and for at least 1 year for recurrent episodes , while sDiagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) [This was a 52-week, open-label, single-arm study of outpatients (aged ≥ 18 years) meeting (DSM-IV)  criteriaThis report focuses upon data taken from the subset of patients aged 65 years and older (n = 101) within the larger study described above.The study protocol was approved by the ethics committee at each site in accordance with the principles of the Declaration of Helsinki. All patients provided written informed consent prior to the administration of any study procedures or study drug. All patients were required to have a Clinical Global Impression of Severity (CGI-S) score ≥ 3 at the screening and baseline study visits.Patients were excluded for the following reasons: a previous or current diagnosis of schizophrenia, schizophreniform disorder, schizoaffective disorder, or bipolar disorder; presence of an Axis II disorder that would interfere with protocol compliance; serious medical illness; taking benzodiazepines on a daily basis for ≥ 2 weeks prior to enrollment; a history of substance dependence within the last year; or a positive urine drug screen. Subjects judged to be at risk for suicide were also excluded.Patients were not permitted to receive other antidepressant, antimanic, or antipsychotic agents during the study. Episodic use  of benzodiazepines was permitted. The use of benadryl, chloral hydrate, cough and cold medications, and narcotics, was allowed on an episodic basis only. Subjects were permitted to take antihypertensives, antiarrhythmics, antibiotics, and multivitamins among other medications while in the study.a priori specified as the primary outcome), the HAMD17 total score [17 subscales , the Beck Depression Inventory-II (BDI-II) [responders if they had a decrease from baseline of at least 50% in HAMD17 total score. Patients were defined as remitters if they had a HAMD17 total score ≤ 7.Efficacy was assessed using the CGI-S scale  (a prior(BDI-II) , and the(BDI-II) . Patient(BDI-II) , which iSafety measures included spontaneously reported adverse events, serious adverse events , vital signs, electrocardiograms (ECGs), and laboratory analyses. Adverse events and vital signs were collected at each visit.Lilly reference ranges were used to define limits for abnormal laboratory values , and potand a decrease from baseline of ≥ 20 mm Hg;(i) Low supine (or standing) systolic BP: ≤ 90 mm Hg and an increase from baseline of ≥ 20 mm Hg(ii) High supine (or standing) systolic BP: ≥ 180 mm Hg and a decrease from baseline of ≥ 15 mm Hg;(iii) Low supine (or standing) diastolic BP: ≤ 50 mm Hg and an increase from baseline of ≥ 15 mm Hg.(iv) High supine (or standing) diastolic BP: ≥ 105 mm Hg Patients were considered hypertensive at baseline if they had a historical diagnosis, secondary condition, or adverse event at the baseline visit consistent with a clinical diagnosis of hypertension or high blood pressure.ECGs were collected at baseline and Weeks 4, 28, 52 or at early discontinuation. Patients at 2 sites in Mexico and 1 site in Columbia also had ECGs over-read by a cardiologist at a central location. For these ECGs, QT intervals were corrected (QTc) using Fridericia's correction (QTcF). All other patients had ECGs read by the site for classification as either normal or abnormal. Limits for PCS QTc values were an increase in QTcF of ≥ 30 msec and any postbaseline value ≥ 450 msec for males or ≥ 470 msec for females .Mean changes from baseline to last observation in laboratory analytes, vital signs, and ECG intervals were assessed using ANOVA with models that included investigator. Longitudinal mean changes and categorical changes  were assessed via a likelihood-based repeated measures approach. Models for mean changes included investigator, visit, baseline value, and baseline-by-visit interaction.Mean change in CGI-S score was compared between younger (age <65) and elderly (age ≥ 65) patients using the repeated measures analysis as previously described, with age group and age group-by-visit interaction added to the model. Differences between young and elderly patients in rates of treatment emergent adverse events were assessed using Fisher's exact test.This report was based on data from 101 patients aged 65 and older. The oldest patient was 87 years of age, while the median age was 70. Patient characteristics at baseline are summarized in Table Mean changes from baseline for all efficacy outcomes were highly significant (p < .001, t-test for mean change) at all assessment times Table . In the A comparison of visitwise mean changes in CGI-S score between elderly patients (age ≥ 65, n = 101) and those patients in the study aged <65 years , personal conflict/other reasons (9.9%), and noncompliance (5.0%). The adverse events leading to discontinuation in >1.0% of enrolled patients at a duloxetine dose of 80–120 mg/d were somnolence (4.0%), dizziness (3.0%), diarrhea (2.0%), hypertension (2.0%), and vomiting (2.0%). Two-thirds of the discontinuations due to adverse events (18/27) occurred within 2 weeks of initiation of therapy.A total of 9 enrolled patients reported serious adverse events during the study. Most of these events were considered by the investigator to be unrelated to duloxetine exposure. The serious adverse events reported by more than 1 patient were hip fracture (2), and confusion (2), while there were single reports of agitation, angina pectoris, cerebrovascular disorder, coronary artery atherosclerosis, dementia, dizziness, hypomania, and myocardial ischemia. Individual occurrences were few, thus no clear temporal pattern of incidence of each event could be determined.Treatment-emergent adverse events occurring in >5% of patients during the open-label therapy phase (Weeks 1 through 52) are summarized in Table Rates of occurrence of other adverse events of importance in an elderly population were low: 2 patients experienced a fall, while there were single reports of syncope and postural hypotension. When analyzed by age group, patients aged 65 and older were found to report a significantly lower incidence of insomnia and headache than those patients aged <65 Table . No otheMean changes from baseline to last observation for standing and supine systolic and diastolic blood pressures were less than 2 mm Hg and not significantly different from zero: supine systolic BP -1.5 mm Hg (p = .364), supine diastolic BP -1.8 mm Hg (p = .141), standing systolic BP -1.9 mm Hg (p = .269), standing diastolic BP -0.1 mm Hg (p = .907). Using repeated measures analysis, mean changes in blood pressure were <4 mm Hg at every visit from baseline to endpoint.A mean change analysis was utilized to compare blood pressure in patients who were hypertensive (n = 40) versus non-hypertensive (n = 58) at baseline. Baseline hypertensive patients exhibited small mean decreases (<4 mm Hg) in both standing and supine systolic and diastolic blood pressures from baseline to endpoint, while patients who were not hypertensive at baseline demonstrated mean changes in these same measures of 0.3 to -1.1 mm Hg Figure .Mean baseline-to-endpoint increases were observed for supine pulse (mean change = 1.6 bpm, p = .105) and standing pulse (mean change = 1.1 bpm, p = .338) but these values did not differ significantly from zero.Rates of occurrence of potentially clinically significant (PCS) values for systolic and diastolic blood pressures were generally low. The incidence of PCS low standing systolic blood pressure was 5/96 (5.2%), while all other assessed blood pressure and pulse readings had incidences of PCS values <2.5%.There were no significant changes in cardiac intervals detected by ECG. Mean changes from baseline to last observation were: PR -3.3 msec (p = .363), QRS -2.5 msec (p = .420), QT 5.0 msec (p = .730), and QTcF 6.2 msec (p = .553). No patient experienced a PCS QTcF value during the course of the study.After 52 weeks of treatment, the mean change in weight from baseline to last observation was -0.1 kg (p = .741), while a mean weight change of +0.3 kg was determined using MMRM analysis  values.All patients who proceeded past Week 52 received no study drug for 2 weeks until Week 54 via abrupt discontinuation (no taper). Discontinuation-emergent adverse events occurring in ≥ 5% of patients were dizziness (8.9%), anxiety (7.9%), headache (5.0%), and insomnia (5.0%).17, BDI-II, PGI-I, SDS) at all visits. By way of comparison, significantly greater improvements for duloxetine compared with placebo were observed in HAMD17 total score, HAMD17 subscales and CGI-S score in two 9-week, placebo-controlled studies of duloxetine (60 mg once daily (QD)) in patients aged 55 years and older [The current analysis focused upon 101 depressed patients aged 65 years and older who received long-term, open-label treatment with duloxetine (80 mg/d or 120 mg/d). Efficacy was demonstrated on all assessed outcome measures, both clinician- and patient-rated. Highly significant improvements were seen in both patient- and clinician-rated depression and health outcome scales , and global improvement  [Onset of efficacy is an important consideration in antidepressant trials, but in the absence of a placebo arm it is especially difficult to define and assess . HoweverS scale) . It has S scale) . Indeed,In this study, observed case response and remission rates following 6 weeks of open-label duloxetine therapy (62.9% and 41.4%, respectively) were comparable to the response and remission rates (52.8% and 44.1%, respectively) observed in older patients in two 9-week double-blind, placebo controlled trials of duloxetine (60 mg QD) . FurtherIn light of the recommendation that elderly patients receive at least 12–18 months of antidepressant therapy , the lonThe incidence and pattern of treatment-emergent adverse events during Weeks 1 to 8 of this study were generally similar to those observed in acute-phase, placebo-controlled trials in older patients . The mosAdministration of medication to elderly patients necessitates consideration of the physiological changes which accompany aging. Such changes can result in substantial differences in adverse event profiles between older and younger patient populations . In thisAntidepressants with benign cardiovascular profiles may be particularly suitable for the treatment of an elderly population, in which heart disease is more prevalent than in younger patients . In thisWeight change is an important consideration in older patients being treated with antidepressants , especiaIt is important to consider all of the safety findings described here in light of the dosing and design requirements of the study. The doses used in this open-label study were up to 2-fold greater than the once-daily 60 mg duloxetine dose which has been shown to provide robust efficacy in older patients in placebo-controlled trials . The dosResults from this open-label study of depressed patients aged 65 and older suggest that duloxetine is safe and well tolerated in long-term use. Statistically significant and clinically relevant improvements in all assessed efficacy measures were observed at each patient visit. Furthermore, the efficacy and adverse event profile of duloxetine appears to be comparable in older (age ≥ 65) and younger patients (age 18–64). These results, together with those obtained from acute phase, double-blind, placebo-controlled trials, support the efficacy of duloxetine in the treatment of major depression in older patients.Drs. Wohlreich, Mallinckrodt, and Watkin are employees of Eli Lilly and Company. Dr. Hay was employed by Eli Lilly and Company at the time of the study.MMW, CHM, JGW, and DPH participated in interpretation of data and drafting of the manuscript. CHM carried out the statistical analyses. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


There has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life.S. cerevisiae and the six Bacterial species in almost all measured higher-level topological properties. Our findings also indicate that the metabolic network in Archaeal species is similar to the exponential random network.Three groups of topological properties were used in this study: network indices, degree distribution measures and motif profile measure. All of which are higher-level topological properties except for the marginal degree distribution. Metabolic networks in Archaeal species are found to be different from those in If these metabolic network properties of the organisms studied can be extended to other species in their respective domains (which is likely), then the design principle(s) of Archaea are fundamentally different from those of Bacteria and Eukaryote. Furthermore, the functional mechanisms of Archaeal metabolic networks revealed in this study differentiate significantly from those of Bacterial and Eukaryotic organisms, which warrant further investigation. Classification of biological organisms is of fundamental importance to evolutionary studies. It is commonly believed that there are three domains of life: Archaea, Bacteria and Eukaryote. Currently, the most popular classification method is the so called "molecular approach", in which polymorphism information in DNA or protein sequence is exploited to assess the phylogenetic relationships among species ,2. To a γ function, which measures the similarity between rankings of nodes in two networks. They studied metabolic and information network structures of 43 organisms using these two measures under the hypothesis that network structure and the network design principle(s) behind them contain phylogenetic information. Ma and Zeng [Podani and co-workers  proposedand Zeng  conducteand Zeng . These dRestricted by the theoretical network structural studies, there are not many deterministic and informative topological measurements available -11. The E. coli and S. cerevisiae have been studied intensively in biochemistry, cell biology and genetics; hence the rebuilt networks in those organisms present the best chance to approximate the true underlying network. Moreover, single cell organisms are less likely to have experienced the Whole Genome Duplication (WGD), which might drastically change the network structure [S. cerevisiae; six Bacteria: E. coli, V. cholerae, R. solanacearum, B. subtilis, L. lactis, S. coelicolor; and four Archaea: S. solfataricus, S. tokodaii, M. acetivorans, T. acidophilum.Single cell model organisms such as tructure ,18. As ain vivo pull down assay or DNA microarray, which are subject to high uncertainties, and the resulting networks may not be good approximation to biological complexity [There are three main types of intracellular networks: the protein-protein interaction network, the transcriptional regulation network and the metabolic network. The first two are rebuilt by using high throughput techniques such as yeast two-hybrid system, mplexity -22. On tmplexity . It is wmplexity ,25 and amplexity . The formplexity . Therefo2O, should be removed from metabolic networks. We followed their suggestions by removing such "current metabolites" before conducting the following analysis.In constructing metabolic networks, Ma and Zeng  argued tS. cerevisiae and the six Bacterial species appear much more heterogeneous than Archaeal metabolic networks. It is well known that the so-called exponential random network  appears homogeneous while scale-free network  appears more heterogeneous and modular [Before checking different types of network topological measurements, we visually compared different metabolic networks Fig. . Metabol modular .S. cerevisiae andthe six Bacterial species are more clustered and modular than those in the four Archaeal species  also indicate that the metabolic networks in es Table , Fig. 2.S. cerevisiae and the six Bacterial metabolic networks are much higher than those in Archaeal metabolic networks , see Methods for details) of three-node subgraphs and four-node subgraphs. The concentration of subgraphs is an objective measure of the extent of clustering and modularity of the network ,9. It isrks Fig. .R2 and 95% individual confidence intervals are summarized in Table γ is around -0.3 in all cases and the estimated log-transformed scaling parameter α ranges within 2.0 to 2.5. These indicate that marginal degree distribution, which is a low-level  topological property measure, although showed some distinction, is not enough to effectively differentiate networks from different domains. Overall, metabolic networks in most of the species we studied seem to follow the power-law distributions and thus are "scale-free". The fact that the incoming degree distributions of most Archaeal species we studied do not follow power-law well  topological properties such as average clustering coefficient . In metaN(K0, K1) is defined as the number of edges connecting nodes of connectivity K0 to those of connectivity K1. For metabolic networks, which are directed, N(Kout, Kin) is used to measure the number of arches where substrate (node) with out-connectivity Kout transforms to product with in-connectivity Kin. This quantity reflects intrinsic properties of the network and can be used to distinguish different types of networks. For instance, we can test whether N(Kout, Kin) of a particular network differs significantly from that of the random network. To be specific, we calculate , where (Kout, Kin) represents the mean of random variable N(Kout, Kin) in a large number (say, 1000) of random networks simulated by an edge-rewiring algorithm proposed by Maslov and Sneppen [(Kout, Kin) denotes the estimated standard deviation of N(Kout, Kin). The p-value can then be obtained by compare Z to a standard normal distribution. Comparing with "properly" randomized network ensembles allows us to concentrate on those statistically significant patterns of the complex network that are likely to reflect the design principle(s) [Joint degree distribution measures and describes correlation between connectivities of neighboring nodes.  Sneppen , (Kout, ciple(s) .S. cerevisiae and the six Bacterial species. Although the dark red regions of the Z-score profiles in Archaeal species are quite different in scale, they all seem to differ significantly from the random network preserving the corresponding marginal degree distribution in a similar way . Looking into the correlation profiles more carefully, we found that the number of statistically significant positive (Kout, Kin) increases in the order of S. cerevisiae, the six Bacterial species and the four Archaeal species. The significant Z-score of certain observation N(Kout, Kin) implies that the chemical reaction between substrates with out-degree Kout and products with in-degree Kin are statistically significant. We define substrates whose Kout >= 2 or products whose Kin >= 2 as versatile metabolites. Thus, the above trend implies that the preference to employ reactions involving versatile metabolites increases in the order of S. cerevisiae, the six Bacterial species and the four Archaeal species. Correspondingly, the variety of metabolites decreases in the above order and so does the number of distinct enzymes or variety of enzymes because of the high specific binding of metabolites and enzyme. This is consistent with the biological facts that S. cerevisiae (Eukaryote) encodes a greater variety of enzymes than Bacterial and Archaeal species.We calculated statistically significant correlation profiles (Z-score profiles, see Methods for details) for the metabolic network in each organism Fig. . The Z-sThe network motif is defined to be recurring and non-random building blocks of the network ,15. JustE. coli transcription regulation network are covered by network motifs [E. coli transcriptional regulatory network, "individual motifs aggregate into homologous motif clusters and a supercluster forming the backbone of the network and play a central role in defining its global topological organization." More importantly, network motifs capture the information that is likely to be missed by the correlation profiles because motif actually describes the number of appearances of certain configurations of multiple nodes, and therefore nicely complement with the correlation profiles [Network motif has been shown to be informative of network design principle(s) and network structure. It was found that over 80% of the nodes in the k motifs . Dobrin k motifs  recentlyprofiles . One migS. cerevisiae and the six Bacterial species are identical while there is no three-node motif found in any of the four Archaeal networks  are shared by the latter  to neurology and engineering fields was not found in any of the four Archaeal metabolic networks  motif found in networks from biology  in Archaeal species might also enabled them to survive in those extreme physiological conditions. Archaeal species might employ some biologically significant subgraphs  which can not be detected by current motif searching algorithm [The metabolic networks in Archaea are both "random-like" and "scale-free", which might exert profound influences on their adaptability to the hostile environment. Archaeal species are typically restricted to marginal habitats such as hot springs or areas of low oxygen concentration and can assimilate different kinds of inorganic carbon and nitrogen sources. Indeed, the chemical structure and component of the macromolecules such as protein and lipid make significant contributions to the organism's adaptability to the environment. The seemingly lgorithm . This maOur comparison results showed that many network structural properties measured in Archaeal species are different from those of non-Archaeal species. However, the hidden anthropomorphic factors might account for some of the differences observed. Specifically, the drastic differences of topological profiles between the metabolic networks of Archaeal species and non-Archaeal species may be partially explained by the fact that significantly less extensive metabolic pathway studies have been conducted in Archaeal species . RobustnS. cerevisiae and the six Bacterial species. This provides further evidence that the metabolic network structures and consequently the design principle(s) in the four Archaeal species are very different from those in S. cerevisiae (Eukaryote) and the six Bacterial species. Our finding that the metabolic networks in Archaeal species possess many properties of the exponential random network begs for better understanding of the design principle(s) in biological networks, which may be revealed by further systematic analyses. For example, locate and align conservative pathways such as glycosis between E. coli or S. cerevisiae and Archaeal species to understand the functional mechanisms of Archaeal metabolic networks.Our network analysis results showed that in most of higher-level  topological properties measured, metabolic networks in the four Archaeal species are similar to each other but significantly different from those in reaction, enzyme, react, connect and organism. We compiled a new table from this database excluding any inconsistent or redundant connections between metabolites (details below). SQL was used to query the database.Chemical reaction data was obtained from metabolic database in Ma and Zeng , which c+ and Nicotinamide is a pair of metabolites in two reactions: 1) NAD+ + L-Arginine = Nicotinamide + N2 (ADP-D-ribosyl)-L-arginine 2) NAD+ + H2O ->Nicotinamide + ADPribose. (Note that here the role of NAD+ is NOT "current" metabolite, and hence connections established through it should NOT be removed). Reaction 1 is a reversible reaction while reaction 2 is not. We annotated an edge between the two metabolites as long as there was at least one reversible reaction that both of them were involved. For example, the type of connection between NAD+ and Nicotinamide is edge (undirected connection). This step could be summarized as "edge ← edge + arc".Inconsistent connections refer to pairs of metabolites that have conflicting reversibility annotation. It is caused by the fact that a pair of metabolites can be in more than one reaction and the reversibility of these reactions can be different. For example, NADN-Acylsphingosine = UDP + Glucosylceramide 2) Glucosylceramide + H2O = D-Glucose + N-Acylsphingosine. (N-Acylsphingosine and Glucosylceramide is a pair of metabolites that switch their roles in two irreversible reactions). In case of redundancy, we annotated an edge between the pair of metabolites rather than the two arcs because they could be converted to each other through two reactions. This step could be summarized as "edge ← arc + arc".There are also numerous redundant connections where the same pair of metabolites switch their roles between substrate and product in two or more different irreversible reactions. For example: 1) UDPglucose + Cin and Cout. Cin measures the average clustering coefficient of the node representing the product that can be generated from its first-order "nearest neighbors" through chemical reactions. Cout measures the average clustering coefficient of the node that generate its first-order "nearest neighbors" through chemical reactions. The larger the coefficients, the more clustered and modular the network appears to be.We define two kinds of clustering coefficients for each node in the directed metabolic networks, i.e. ni in the network is defined as , where gjk is the number of shortest paths between node j and node k. gjk(ni) is the number of shortest path between node j and node k containing node ni, g is the total number of nodes with edges/arcs. CB(ni) needs to be multiplied by two in the case of directed network [. Higher value of betweenness indicates the network is more clustered and modular.The betweenness for any node  network . The ave, where d(j, k) is the shortest path length between node j and node k (distance), V represents the set of all nodes with edges/arcs of the graph, and g is the number of nodes with edges/arcs.Watts and Strogatz  defined G is the longest geodesic between any pairs of nodes. The geodesic is the shortest path between a pair of nodes. Pajek [The diameter of the directed graph s. Pajek  was usedGs is a subgraph of G if the set of nodes of Gs is a subset of the set of nodes of G, and the set of lines in Gs is a subset of the lines in the graph G. Let M be the number of subgraphs, and N be the number of nodes with edges or arcs. Then the "concentration of subgraph" is defined as C = M/N. A high value of C indicates the network is more clustered and modular. Mfinder1.1 [M and N.Wasserman and Katherine  defined inder1.1  was usedA, a matrix of 0 or 1. Zero means there is no connection between nodes, and 1 the opposite. The outgoing degree of the node i, kout(i) is defined as , where . The incoming degree of the node i, kin(i) is defined as .The marginal degree distribution of each network is calculated from the Boolean adjacency matrix P(Ki) = γ log(Ki) + log(α) + εi (i = 1,2,...,n), γ and α are parameters, εi is the residual. Ki is the degree and P(Ki) is the corresponding probability. Based on the fitted linear model, we made statistical inference including parameter estimation and individual confidence intervals on the estimates using the Least Square method.The power-law degree model was first log transformed into linear model, i.e. log Statistically significant correlation profiles were calculated using Matlab code downloaded from Dr. Maslov's website . The adjp-value is smaller than a pre-specified threshold, e.g. 0.01. 2) The number of appearances in real networks with distinct sets of nodes is larger than another pre-specified cut-off value, e.g. 4. 3) The number of appearances in real networks is significantly larger than that in randomized networks, i.e. . Nreal and Nrand represent the number of certain subgraphs detected in real-life network and randomized networks, respectively. This is to avoid the situation where some common subgraphs are detected as motifs that have only slight differences in Nreal and Nrand but have a narrow spread of distribution in randomized networks [According to Milo et al. , a subgnetworks ,15. Motinetworks .DZ and ZSQ conceived and designed the study; DZ wrote the computer code, analyzed the data and draft the manuscript. Both authors read and approved the final manuscript.Four-node motifs found in the metabolic networks in different species. The number of connecting nodes for each network is shown. For each motif, the numbers of appearances in real networks  and in randomized networks  are shown. The p-values of all motifs are less than 0.01, as determined by comparing to 1000 randomized networks. Each motif occurs at least four times in one network. Other restrictions apply. Motifs were detected and generated using program found in Milo et al. [o et al.  and the Click here for file


Practice management education continues to evolve, and little information exists regarding its curriculum design and effectiveness for resident education. We report the results of an exploratory study of a practice management curriculum for primary care residents.After performing a needs assessment with a group of primary care residents at Wright State University, we designed a monthly seminar series covering twelve practice management topics. The curriculum consisted of interactive lectures and practice-based application, whenever possible. We descriptively evaluated two cognitive components (practice management knowledge and skills) and the residents' evaluation of the curriculum.The mean correct on the knowledge test for this group of residents was 74% (n = 12) and 91% (n = 12) before and after the curriculum, respectively. The mean scores for the practice management skill assessments were 2.62 before (n = 12), and 3.65 after (n = 12) the curriculum (modified Likert, 1 = strongly disagree, 5 = strongly agree). The residents rated the curriculum consistently high.This exploratory study suggests that this curriculum may be useful in developing knowledge and skills in practice management for primary care residents. This study suggests further research into evaluation of this curriculum may be informative for practice-based education. Practice management education for residents has traditionally included training physicians in management issues related to the practice environment, including fiscal management, leadership skills, business and management skills, and managed care concepts . ManagedA few studies on practice management curricula exist for resident education, but much more information is needed on successful curriculum design and evaluation -13. We dWe developed the curriculum for the University Medicine/Pediatrics Practice (UMP). This practice is a primary care-oriented, faculty-resident practice on the campus of Wright State University. Thirteen internal medicine/pediatrics residents, five general internist faculty, two internal medicine/pediatrics faculty, and one pediatrics faculty practice here. The practice is managed by Premier Healthnet, a 100 physician multi-site primary care group. Although UMP's mission includes addressing the needs of indigent patients in the Dayton area, it is modeled after a community-based, teaching practice model. Therefore, faculty and residents are expected to use effective practice management skills in their individual practices. A typical resident from our program enters a small (1–5 physician) community practice upon graduation and practices both internal medicine and pediatrics.In 2001, two faculty members (GEC and RJS) at the Wright State University Departments of Medicine and Community Health were identified as lead faculty for this curricular project. Primary care faculty in the Departments of Pediatrics and Medicine tasked these two lead faculty members to development a practice management curriculum to reflect the evolving practice theme. By "evolving practice theme," we mean teaching practice management topics similar to the Tuft's curricular theme.One lead faculty member (GEC) performed a needs assessment on the Internal Medicine/Pediatrics residents at UMP in the spring of 2001. The assessment used qualitative analysis via informal interviews of two senior residents, one of whom was chief resident. The interviews included open-ended questions on the need for practice management knowledge (example question: "What do you need to learn this year to help prepare you for running a community practice?"). The needs assessment also included an open feedback session with the residents after discussion of potential topics at the monthly resident education meeting (majority of residents present).The results of the need assessment were uniform; the residents felt inadequately trained in practice management. The lead faculty concluded that these residents had some training in a few specific content areas (i.e., coding), but lacked an overall basic practice management knowledge or skill.The lead faculty met again in mid-2001 to design the curriculum. The goals of the curriculum were to give the residents a basic understanding of practice management concepts and skills in the evolving practice environment. The lead faculty were free to select the most effective methods to meet their goals. They did face some challenges. They were given only 30-minute time slots each month and had 12 months to accomplish these goals. They also had to show some objective evidence of its effectiveness and have support of the residents at the end of the 12 months to continue the curricular project.The design process resulted in a series of seminars covering 12 topics, listed in Table The respondents were a convenience sample of Internal Medicine/Pediatrics residents from all four years of training. We used a pre-experimental (one-group pretest/posttest) design for this exploratory study. We descriptively evaluated the curriculum on two cognitive components: practice management knowledge and skills. We also assessed the residents' evaluation of the curriculum.To evaluate practice management knowledge, we used a knowledge test consisting of identical 12 item , and each question covered one objective from each topical area from Table To evaluate practice management skills, we devised a survey of self-assessed practice management skills. The survey consisted of 12 statements, and each statement queried the residents to respond on their assessment of their own practice management skills. Each statement consisted of one specific skill from an objective from each topical area listed in Table To explore the residents' evaluation of the curriculum, we devised another survey. This survey consisted of four statements querying the residents on their overall assessment of this curriculum and practice management education in general. The statements from the survey are given in Table The process of test instrument development was the same for both the knowledge test and the self-assessed skills survey. One lead faculty member (GEC) would generate a list of candidate items based on each objective in Table th-year; 3, 3rd-year; 4, 2nd year; and 4, 1st-year residents). A third year resident failed to complete the 0-month tests and surveys, and a first year resident failed to complete the 12-month tests and surveys. This left 12 responses for both sets (0- and 12-month) tests and surveys. The average attendance for the sessions was 12, with a range of 10–13 attendees.The participants were the 13 Internal Medicine/Pediatrics residents, and represented all four years of training .Second, we were interested if the missing data on the 0-month and 12-month data could have impacted the results. Since one third-year resident completed the 12-month but not the 0-month test, we were interested in exploring if his responses on the 12-month test could have caused a larger difference between these two tests. After censoring his data, there appeared to be little impact on the 12-month results (censored mean score = 0.91, censored 95% CI, 85–96%). Additionally, the first year resident who failed to complete the 12-month test may have also impacted the results. Due to loss of identity links, we could identify her data to censor from the 0-month test. However, we censored the lowest score on the 0-month test as representing hers (this assumes that her score lowered the 0-month data the most, and, therefore, had the largest impact on 0-month mean score by skewing it away from the 12-month mean score). After censoring this data, we found no significant change in the 0-month results (censored mean score 0.75, censored 95% CI, 70–80%).The results for the self-assessed skill survey are given in Table The results of the curriculum evaluation survey are given in Table The practice environment continues to evolve . AlthougWe were interested in whether a curriculum design with this evolving theme may be useful in primary care education. We describe, in detail, a curriculum design similar to the evolving theme designed for a small group of primary care residents. The advantage of such a program as ours is its detailed design based on general and specific needs assessments and a description of evaluation methodologies. Our data suggests that this intervention may have had an impact on resident knowledge scores and self-assessed skills. Additionally, the residents appeared remarkably positive towards this practice management curriculum and practice management education in general.A few studies have been published on practice management curricular design and evaluation for primary care residents. In a response to the growing need physician-managers, both Zoorob and Taylor and Johnson described curricular designs they proposed would fill this need ,14. LyncOur study does have some limitations. Because our small sample size, formal hypothesis testing was not possible and our data remains descriptive only. Stronger conclusions of these results would need to be re-evaluated with more subjects using formal hypothesis testing methods. Our experience suggests that internal motivation was probably one key factor to acceptance and apparent acceptance of this curriculum; the request for developing the curriculum came from our residents themselves. Also, the UMP faculty is uniformly positive towards developing these skills in themselves and in the residents, and this probably influenced residents' motivation to learn the subject matter. Since our faculty-resident practice is based on a primary care, community model, it may be difficult to generalize it to hospital-based practices or specialty residency training.We conclude that an extended curriculum in practice management with an evolving practice theme may be useful in primary care education. We also believe that attention to instructional design, including performing a needs assessments, using many teaching methods, and applying the concepts learned in learners' practices, may contribute to its acceptance and success. Future educational designs for this curriculum include its continued expansion, exploring more educational opportunities for implementation, and addressing specific characteristics of success and failure. Future educational research in this area would require a more formal research design to derive stronger conclusions regarding its effectiveness.None declared.GEC participated in the curricular needs assessments, curricular design, curriculum implementation, and drafting of the manuscript.RJS participated in the curricular design, curriculum implementation, and drafting of the manuscript.The pre-publication history for this paper can be accessed here:


To study the cellular distribution of CCN3(NOV) and to determine if the carboxyterminus of CCN3 is hidden or masked due to high affinity interactions with other partners. CCN3 was detected using affinity purified antibodies (anti-K19M-AF) as well as a Protein A purified anti-K19M antibodies (anti-K19M IgG) against a C-terminal 19-aminoacid peptide (K19M) of human CCN3 protein. The antibodies were applied in indirect immunofluorescence tests and immunoenzyme assays on glial tumor cell line, G59, and its CCN3-transfected variant G59/540 and the adrenocortical cell line, NCI-H295R.Anti-K19M-AF antibodies reacted against K19M peptide in ELISA and recognized two bands of 51 kDa and 30 kDa in H295R  cell culture supernatants by immunoblotting. H295R culture supernatants which contained CCN3 as shown by immunoblotting did not react with anti-CCN3 antibodies in liquid phase. Anti-CCN3 antibodies stained the surface membranes of non-permeabilized H295R and cytoplasm in permeabilized H295R cells. Similarly, anti-CCN3 stained surface membranes of G59/540, but did not react with G59 cells. Prominent cytoplasmic staining was observed in G59/540, as well as the cell footprints of G59/540 and H295R were strongly labeled.The K19M-AF antibody directed against the C-terminal 19-aminoacid peptide of CCN3 recognized the secreted protein under denaturing conditions. However, the C-terminal motif of secreted CCN3 was not accessible to K19M-AF in liquid phase. These anti-CCN3 antibodies stained CCN3 protein which was localized to cytoplasmic stores, cell membranes and extracellular matrix. This would suggest that cytoplasmic and cell membrane bound CCN3 has an exposed C-terminus while secreted CCN3 has a sequestered C-terminus which could be due to interaction with other proteins or itself (dimerization). Thus the K19M-AF antibodies revealed at least two conformational states of the native CCN3 protein. The CCN3 protein belongs to an emerging family of growth regulators referred under the CCN acronym  .The CCN3 (NOV) gene had been initially characterized as an integration site for the myeloblastosis associated virus MAV  which inThe CCN proteins share a strikingly conserved multimodular organization with distinctive functional features . From thThe multimodular structure of CCN3 and other CNN proteins raises interesting questions as to participation of each individual module in conferring the biological properties to the full length proteins. Either the biochemical functions of the individual IGFBP, VWC, TSP and CT modules are indeed conserved and in sum determine the ultimate function of the full length protein, or each module confers on the whole protein specific biological functions which may vary from the conserved function, and either substitute or add to those of individual modules.Application of the yeast two-hybrid system and co-precipitation strategies to identify proteins interacting with CCN3 has revealed that full length CCN3 interacts with several receptors, signaling molecules, and proteins of the extracellular matrix (16–19), suggesting functional involvement of CCN3 in cell signaling and adhesion regulation.Our results also established that truncated isoforms of CCN3 could bind specific targets and pointed the CT domain of CCN3 as a critical determinant for protein interaction. This led us to hypothesize that truncated isoforms of CCN3 could also modulate its biological activity (3). The question therefore arises whether different conformational states exist due to multiple protein interactions and thereby the presentation of known antigenic epitopes.In the present study we have used an immunological approach to establish the cellular distribution of CCN3 in cell lines representing adrenocortical and glioblastoma tumors and to ask whether the CT module of CCN3 exists in different conformational states depending on its involvement in protein interactions and cellular location. We now provide evidence that the CT end of CCN3 exists in more than one conformational state.Cell culture supernatants and cellular lysates from the H295R, G59/540, and parental G59 cell lines were electrophoresed under denaturing conditions and immunoblotted with anti-K19M IgG antibody. Immunoblot analysis revealed secreted forms of CCN3 for the H295R and G59/540 cell lines, consisting of two distinct bands at 51 kDa and 30 kDa Figure . The latWhen tested in ELISA, pre-incubation of the anti-K19M-AF antibodies with CCN3-containing H259R supernatant did not affect the binding of anti-K19M-AF antibodies to the K19M peptide coated on microtitre plates .Paraformaldehyde fixed, non-permeabilized H259R cells treated with the anti-K19M antibody (Protein A purified) exhibited immunofluorescent membrane specific staining distributed over the cell surface Figure.  while siIn summary, the results produced using an immunological approach would suggest multiple conformations of the C-terminal end which harbors the immunogenic epitopes. Furthermore, these variations are associated with cell bound and secreted forms of CCN3. Cytoplasmic localization indicated abundant CCN3 protein localized with ER and Golgi networks.In this study we exploited the range of binding affinities present in polyclonal antibodies raised against the C-terminal peptide of CCN3 and analyzed with different immunoaffinity methods to ask whether CCN3 exists in alternate conformational forms in cell cultures and supernatants. Whereas immunocytochemistry of fixed, permeabilized and non-permeabilized cells yielded evidence of both cell surface membrane and cytoplasmic expression and topographical distribution of native CCN3, ELISA method and western immunoblot revealed different possible conformational forms of CCN3. Taken together the results of the assays suggest that native CCN3 assumes different configurations that either expose or sequester the C-terminal peptide depending on whether CCN3 is cell associated or free within the culture supernatant. When considered in the light of recent evidence indicating that CCN3 can associate with specific integrins at the cell membrane ,24 the qin vitro that CCN3 is sequestered in cell footprints representing a secreted extracellular matrix. Cell footprints can be deposited by various cell types in different arrangements and consists of extracellular matrix, including a variety of basement membrane proteins [In these studies we focused on two cell lines, H295R and G59, representing adrenocortical and glial tissues, significantly different in their anatomical location and microenvironment. Since CCN3 has been demonstrated in plasma  it is coproteins -32. In oproteins , in turnThis then raises the notion that proximity of CCN3 to cell surfaces could allow CCN3 to function in possible autocrine and paracrine mechanisms. Depending on the associating proteins, CCN3 would likely undergo specific conformational changes with potentially different functional outcomes. A variety of functional states may exist since CCN3 is expressed in secretable and non-secretable isoforms and contains motifs that overlap with other proteins and therein, additional binding partners ,4. Thus The biological significance of different conformations of CCN3 is not known. However, examples from other studies have suggested that conformational changes can occur in serum proteins due to binding of bivalent cations . Since wAltered expression of CCN3 in a variety of cancers may reflect maintenance of a normal homeostatic function of the cell of origin, or may indicate requirement of specific CCN proteins for maintaining the undifferentiated tumor state. One such example where the two possibilities are not yet resolved is in Wilms tumor, where CCN3 is abundantly expressed during normal nephrogenesis and in tumors . InteresOur preliminary investigations here have revealed possible physical and functional states of native CCN3 localizing to cytoplasmic, cell membrane and extracellular matrix. Further complexity is added since shorter and larger isoforms of CCN3 can be detected using western blotting. The origin of these short forms is still not fully understood. As there do not appear to be alternate transcripts  this sugFinally, cells can coordinately express a variety of CCN proteins that are closely related, for example CCN1-3 with cross-over and opposite functional effects yet bearing similar functional domains. Evidence is starting to surface that they might compete for binding partners, such as integrins, thus forming protein complexes with different biological consequences to cell behavior ,19. It wNCI H259R (American Type Cell Collection) is a human adenocortical carcinoma cell line and was cultured in DMEM/F12 supplemented with 2.5 % Nu-serum plus ITS+ supplement (Sigma Co, St. Louis, USA). H295R cells have been characterized and were shown to secrete high levels of CCN3 protein ,21. The Antibodies against C-terminal peptide K19M were used in these experiments after either purification by an antigen specific affinity chromatography (anti-K19M-AF) or by Protein A chromatography (anti-K19M IgGs).3 pH 9.0 containing 0.5 M NaCl (coupling buffer) and added to 3.5 ml CNBr-Sepharose swelled gel and the mixture was rotated end-over-end for 2 hours at room temperature. Excess ligand was eluted with 20 ml of coupling buffer and the gel was incubated in 0.1 M Tris-HCl buffer pH 8.0 for 2 hours at room temperature. The gel was washed 5 times in cycles consisting of 20 ml of 0.1 M acetate buffer pH 4.0 followed by 20 ml of 0.1 M Tris-HCl buffer pH 8.0, each containing 0.5 M NaCl, and then packed into a PD-10 column.The K19M C-terminal peptide (KNNEAFLQELELKTTRGKM) of human CCN3 protein was coupled to CNBr activated Sepharose 4B  following the protocol recommended by the manufacturer. Briefly, 3 mg of peptide were dissolved in 5 ml of 0.1 M NaCOThe rabbit anti-K19M antiserum was absorbed with 1 mg/ml human serum albumin to remove cross-reactivity with human plasma and dialyzed overnight at 4°C against phosphate buffer pH 7.0. An aliquot of 3.5 ml serum was loaded on the affinity column and the flow through and the unbound proteins were collected in 3 ml fractions followed by thorough washing of the column with the loading buffer. The K19M peptide bound antibodies were eluted with 9 ml of 0.1 M Tris-glycine buffer pH 2.8 in 3 ml fractions that were collected into test tubes containing 100 μl 1 M Tris buffer pH 8.0. All column fractions were tested by ELISA for the presence of antibodies reacting against K19M peptide and the positively reacting fractions were further purified by affinity chromatography on pre-packed HiTrap Protein A columns  as recommended. Affinity purified antibody preparations were further tested to determine their reactivities and specificities. The affinity purified anti-K19M-AF antibodies reacted against the K19M peptide when tested in serial dilutions in ELISA (see below). The titers of K19M-AF antibodies were comparatively lower as compared to the unfractionated K19M antiserum. This finding was not unexpected as it likely reflects the polyclonal composition of the primary rabbit antiserum and differences in the content of the specific monoclonal specificities in the antiserum. Importantly, the affinity purified antibodies recognized the K19M peptide when coated on a solid phase.202. The enzyme reaction was stopped by addition of 50 μl/well of H2SO4 and the color reaction was read at 492 nm in a MicroELISA reader.Affinity purified antibodies were titered by ELISA. Individual wells of polystyrene 96-well flat bottom plates (NUNC) were coated with 1 μg/ml of K19M peptide diluted in coating buffer (0.05 M carbonate buffer pH 9.6) by incubation overnight at 4°C. The unsaturated protein binding sites were blocked with 300 μl/well 2% BSA for 1 h at room temperature. The primary anti-K19M antiserum and affinity purified antibodies were added in serial dilutions in duplicates and the wells were incubated for 2 h at room temperature. After thorough washing the wells were incubated with goat anti-rabbit IgG serum conjugated with peroxidase (Sigma Co) diluted 1/5000 in blocking buffer for 1 h at room temperature. The bound enzyme activity was revealed by adding the enzyme substrate 0.5 mg/ml ortho-phenylenediamine in citrate buffer pH 5.0 containing 0.5 μl/ml H2O2 in distilled water for 7 min at room temperature followed by 3 × 5 min washes in PBS, pH 7.2. Non-specific binding was blocked with 1% bovine serum albumin (BSA) for 1 h at room temperature. Cells were washed once in PBS and incubated with K19M-AF diluted in 1%BSA for 2 h at room temperature. After 3 × 10 min washes in PBS goat-anti rabbit IgG conjugated with peroxidase diluted 1/10000 in 1% BSA-PBS was added for 1 hour at room temperature. The cells were washed again in PBS and the bound enzyme activity was developed by adding ortho-phenylendiamine (5 mg/10 ml citrate buffer, pH 5.0) containing 5 μl of 30% H2O2. The color reaction was stopped by adding 50 μl of 10% H2SO4 and the intensity was read at 492 nm in a MicroELISA reader.Tumor cell lines were cultured in complete medium in 96-well flat bottom plates (Corning) to form a subconfluent monolayer and further incubated overnight in serum free medium. The cells were washed 3 times for 5 min each with phosphate buffered saline (PBS, pH 7.2) and cells were fixed by treatment with ice-cold methanol for 30 min warming to room temperature. The endogenous peroxidase activity was blocked with 3% HTo prepare proteins for immunoblotting, cells were lysed in NP40 buffer (50 mM Tris hydrochloride, pH 8.0, 150 mM NaCl, 5 mM EDTA and 2% NP40) with protease inhibitors (Cocktail Tablets, complete, Roche) and phosphatase inhibitors (50 mM NaF, 2 mM sodium orthovanadate) for 30 min at 4°C. After centrifugation at 15 000 g, extracts were stored at -80°C until use. CCN3 proteins in the conditioned medium were concentrated on Heparin Sepharose  as described by Chevalier et al (1998). Briefly, supernatants were incubated overnight with heparine and then washed 4 times in PBS containing protease inhibitors. Bound CCN3 was dissociated using 2-mercaptoethanol in Laemmli buffer, boiled for 10 min and then centrifuged to keep the free protein. Heparin Sepharose concentrated samples and cellular extracts were subjected to electrophoresis under reducing conditions in 12.5% polyacrylamide gels. Separated proteins were subsequently transferred to nitrocellulose by a semi-dry blotter (LKB Biotech, Sweden) as recommended by the supplier. The nitrocellulose sheet was blocked by incubation for 1 hour at room temperature with 5% nonfat milk in PBST (PBS with 0.2% Tween 20, pH 7.4). The membrane was then incubated in the same buffer with the anti-K19M IgG (1/2000) and then washed extensively. The blots were further incubated in goat anti-rabbit IgG conjugated with peroxidase (1/10 000 in blocking solution, Sigma Co, USA) for 1 hour at room temperature. Revelation was performed using the chemoluminescence protocol and reagents (Pierce, Rockford, IL, USA).Cells were grown on alcohol flamed coverslips, rinsed in PBS and fixed in cold 70% ethanol containing 10% formalin (Sigma) for 10 min on ice, and stored in PBS. Immunofluorescence labeling was performed at room temperature. For this procedure, coverslips were placed into weighing boats [Sigma; 4.5cm × 4.5cm] maintaining cell side up. Cells were further permeabilized in 0.5% Triton X-100 in PBS for 15 min and then blocked in 5%FBS/PBS for 30 min.Anti-K19M IgGs antibodies were applied at 1:1000 dilution in 5%FBS/PBS for 1 hour with intermittent rotation, followed by 5 washes in PBS containing 0.1% Tween 20. Subsequently, the cells were incubated in anti-rabbit IgG serum conjugated with either Alexa 488 (green fluorescence) or Alexa 594 (red fluorescence), in 5%PBS/BSA for 1 hour. After final washes in PBS/Tween 20 followed by one wash in PBS cells were mounted with antifade mounting medium (Bio-Rad, France), excess liquid adsorbed with filter paper, and coverslips sealed with clear nail polish. Immunofluorescence images were captured on 400 ASA film and processed further with Adobe Photoshop (version 7.0).None.None declared.SK carried the affinity chromatography and immunoenzyme experiments;HY carried out immunofluorescence labeling experiments;AMB carried out IgG purification and Western blots;BP conceived the study design and coordinated and edited the manuscript