To describe functional outcomes, care needs and cost-efficiency of hospital rehabilitation for a UK cohort of inpatients with complex rehabilitation needs arising from inflammatory polyneuropathies.186 patients consecutively admitted to specialist neurorehabilitation centres in England with Guillain-Barré Syndrome (n = 118 (63.4%)) or other inflammatory polyneuropathies, including chronic inflammatory demyelinating polyneuropathy (n = 15 (8.1%) or critical illness neuropathy (n = 32 (17.2%)).Cohort analysis of data from the UK Rehabilitation Outcomes Collaborative national clinical dataset. Outcome measures include the UK Functional Assessment Measure, Northwick Park Dependency Score (NPDS) and Care Needs Assessment (NPCNA). Patients were analysed in three groups of dependency based on their admission NPDS score: ‘low’ (NPDS<10), ‘medium’ (NPDS 10–24) and ‘high’ (NPDS ≥25). Cost-efficiency was measured as the time taken to offset the cost of rehabilitation by savings in NPCNA-estimated costs of on-going care in the community.The mean rehabilitation length of stay was 72.2 (sd = 66.6) days. Significant differences were seen between the diagnostic groups on admission, but all showed significant improvements between admission and discharge, in both motor and cognitive function (p<0.0001). Patients who were highly dependent on admission had the longest lengths of stay (mean 97.0 (SD 79.0) days), but also showed the greatest reduction in on-going care costs (£1049 per week (SD £994)), so that overall they were the most cost-efficient to treat.Patients with polyneuropathies have both physical and cognitive disabilities that are amenable to change with rehabilitation, resulting in significant reduction in on-going care-costs, especially for highly dependent patients. Guillain-Barré Syndrome (GBS) and other inflammatory polyneuropathies are a group of disorders that are often associated with significant long-term disability Inflammatory polyneuropathies are a clinically and pathophysiologically heterogeneous group. GBS is an acute, autoimmune condition, with a natural course fast with high disability and usually episodic immune treatment; Chronic inflammatory demyelinating polyneuropathy (CIDP) has a chronic, autoimmune, natural course, slow with ongoing disability, usually maintenance immune treatment; Critical Illness Neuropathy (CIN) is more a myopathy than a neuropathy; it is an acute, ischemic/degenerative/inflammatory disorder, associated with a prolonged period of critical illness – usually in intensive care settings. It has no immune treatment option. Its natural course natural course represents resting disability with slow recovery and cognitive deficits due to the systemic inflammatory/infectious etiology. Given this heterogeneity, differences in outcome are expected between these groups.The existing literature tends to focus on GBS, where a number of studies have explored outcomes relating to predominantly physical disability ) or quality of life measures One of the key challenges of healthcare services across the world is to identify those services that are not only effective, but also represent value for money. Porter and Teisberg In England, the UK Rehabilitation Outcomes Collaborative (UKROC) database collates episode data for inpatients admitted to specialist rehabilitation services. In addition to providing the commissioning dataset, it also routinely provides national benchmarking on quality, outcomes and cost efficiency of rehabilitation. Within the dataset, functional gain is evaluated using the UK Functional Assessment Measure (UK FIM+FAM) The aim of this paper was to validate the factor structure of the UK FIM+FAM within the study population and to describe functional outcomes, including change care needs and cost-efficiency following specialist rehabilitation for patients with complex disability arising from inflammatory polyneuropathies. We also examined care needs and outcome across the different diagnostic groups and compared outcomes and co-efficiency of rehabilitation for patients with different levels of dependency.Rehabilitation Services in England are classified into three different levels. Level 1  and Level 2 (district or supra-district) specialist rehabilitation services take a selected population of patients with complex needs for rehabilitation that are beyond the scope of their local (level 3) general rehabilitation services http://www.csi.kcl.ac.uk/ukroc.html. Data collection is now a mandatory requirement for commissioning of specialist rehabilitation services in England, but between 2010 and 2013 it was voluntary and contributing centres could report one of three measures, the Barthel Index, the FIM or UK FIM+FAM. Between 1.1.10 and 30.4.13, a total of 319 inpatient episodes were recorded for patients admitted with a diagnosis of Guillain-Barre Syndrome or other inflammatory polyneuropathy to a total of 56 specialist rehabilitation units. Of these, 217 were admitted to services that record the UK FIM+FAM and had complete UK FIM+FAM admission scoring. Of these 186 patients  had complete UK FIM+FAM scores at both admission and discharge and were taken as the study sample extracted for this analysis. This sample included data from 45 specialist rehabilitation units, representing approximately 75% of the total number of specialised inpatient neurorehabilitation centres (level 1 and 2) within England during the study period.The UKROC database collates the national clinical dataset for in-patient specialist (Levels 1 and 2) rehabilitation in England. The dataset comprises socio-demographic and process data  as well as clinical information on rehabilitation needs, inputs and outcomes. Full details may be found on the UKROC website The FIM+FAM is a global measure of disability that includes the 18-item FIM (version 4) and adds a further 12 items, mainly addressing psychosocial function so that the total FIM+FAM comprises 30 items (16 motor items and 14 cognitive items). Each item is scored on a seven-point ordinal scale from 1  to 7 (complete independence). Originally developed in the US in the 1990 s A separate optional 6-item Extended Activities of Daily Living (EADL) module has been developed with the aim to extend the upper range of the tool The Northwick Park Dependency Score (NPDS) is an ordinal scale of dependency on nursing time (number of helpers and time taken to assist with each task) designed to assess needs for care and nursing in clinical rehabilitation settings Basic Care Needs (BCN) Special Nursing Needs (SNN)  or 5 (present)).The NPDS is shown to be a valid and reliable measure of needs for care and nursing in rehabilitation settings Low dependency (NPDS <10): patients are largely independent for basic self care,Medium (NPDS 10–24): patients generally require help from one person for most self-care tasksHigh (NPDS ≥25): patients require help from two or more persons for most care tasks and often also have special nursing needs.The NPDS also translates via a computerised algorithm to Northwick Park Care Needs Assessment (NPCNA) Although there is no formal accreditation process for use of the UK FIM+FAM and NPDS, the attendance of UK FIM+FAM training by at least a core team of staff is requirement for UKROC registration. All units that are registered with UKROC have access to free training and updates in workshops that are run several times a year to keep staff up to date, and telephone support is also provided by the UKROC team.Within the UKROC dataset, the cost efficiency is calculated as the time taken to offset the cost of rehabilitation by the resulting savings in the cost of on-going care in the community. This is calculated from ‘Mean episode cost of rehabilitation’ divided by ‘mean reduction in weekly cost of care’ from admission to discharge, as estimated by the NPCNA. The cost of episode was calculated per patient as bed-day cost multiplied by length of stay in days. In this study, the cost per bed-day was calculated retrospectively based on the same costing methodology as our previously published cost analysis 16, because it was not previously validated specifically in this population, we carried out a preliminary principal component analysis (PCA) to confirm the overall factor structure. We used admission and discharge scores data pooled together, N = 368 to extend across the scoring range and to allow for a minimum sample size of 10 cases per item. To assess internal reliability, we calculated Cronbach’s coefficient-alpha for the whole scale and subscales as determined by the PCA. A coefficient-alpha between 0.70 and 0.95 is considered to reflect good internal consistency. The factor structure was then tested using confirmatory factor analysis in IBM SPSS AMOS 21. Goodness of fit was assessed with four indices: root-mean-square error of approximation RMSEA, comparative fit index/Tucker-Lewis index CFI/TLI and goodness of fit index GFI. Due to the sample size, the chi-square difference test was not considered a relevant fit index.As the dataset was of reasonable size and the data near normally distributed, parametric techniques were used and statistical analysis was carried out using IBM SPSS statistics v21. Although the UK FIM+FAM has been validated using UKROC data for a general neurorehabilitation samplePaired T tests were used to compare differences in scores between admission and discharge. To identify differences between groups, we used one-way ANOVA with Bonferroni correction. For all statistical tests, two-tailed p values of <0.05 were considered to be statistically significant. Chi-squared test was used for categorical data. ‘FIM+FAM splats’ (radar charts) were employed to show item-by-item changes between mean scores at admission and discharge.The UKROC database collates de-identified data as part of routine clinical practice and the programme registered as a Payment by Results Improvement Project. The analysis of this routinely-collected data is classed as service evaluation, which does not require research ethics permission in the UK.Demographics of the UKROC data and study population are shown in The study sample comprised 116 male and 70 female patients with a mean age at admission of 53.6 (sd = 16.2) years, range 18–85 years. The mean rehabilitation length of stay was 72.2 (sd = 66.6) days. There were 118 (63.4%) patients with Guillain-Barré Syndrome (GBS), 15 (8.1%) patients with CIDP and 32 (17.2%) patients with CIN and myopathy. A further 37 (11.1%) had unspecified neuropathies.16 we rotated, using Varimax procedure, two main factors, representing the motor and cognitive dimensions (accounting for 45.9% and respectively 14.5% of the variance). Four items  which loaded significantly (>0.3) onto both factors, were assigned on the basis of best clinical fit . Most of the items loaded strongly on the first principal component (26 out of 30 items above 0.40). Based on the previous researchical fit . The CroThe mean reduction in overall dependency  was 13.7 ((%%CI 11.1–16.2), of which the majority of change was in the Basic Care Needs section ). This translates into a reduction of 37.4 care hours per week with mean cost savings £720.3 (SD £891.3) per week.Comparison of cost efficiency between the diagnostic groups  demonstrThis multi-centre analysis reports the first application of the UK FIM+FAM and Northwick Park Dependency scores to assess the functional outcome and efficiency of rehabilitation in patients with GBS and other polyneuropathies. Our findings show that, across all diagnostic and dependency subgroups, these patients make significant functional gains during rehabilitation.As expected the majority of patients had predominantly physical disability on admission, and made substantial gains in motor function during the course of their rehabilitation programmes. Somewhat less expected was the extent of disability in cognitive/psychosocial items, which was most evident in the CIN patients, but by no means confined to this group. There is been considerable debate in the literature as to whether the FIM+FAM provides added value over the FIM as an outcome measure for complex rehabilitation. In terms of overall statistics, the FIM+FAM appears to add little benefit The EADL module of the UK FIM+FAM is a relative recent addition (published in 2009) that was introduced to extend the ceiling of the FIM+FAM and to address independence in activities that are important for successful community re-integration Cost-efficiency of rehabilitation was evaluated in terms of the length of time taken to offset the costs if the in-patient rehabilitation programme by savings in the cost of on-going care in the community. For the sample as a whole, costs were offset within just 12 months, but the timeframes ranged from 11–22 months across the different levels of dependency. These periods are relative short compared with the figures of 16–39 months in an equivalent population of patients undergoing rehabilitation following complex acquired brain injury (ABI) We also interrogated the dataset for differences within the diagnostic subgroups of patients with inflammatory polyneuropathies. Patients with CIN were the most dependent and also had more severe cognitive/psychosocial disability than the other groups, both on admission and discharge. This may reflect the multiple insults that many patients in this group will have experienced in the course of prolonged stays in intensive care. However, despite their higher levels of dependency, the CIN patients took the least time to offset the cost of rehabilitation (7.4 months), confirming that specialist rehabilitation is highly cost-efficient in this group of patients.Although the UK FIM+FAM was the primary functional outcome for this study, we also report the change in FIM scores for the purposes of comparison with other studies in the literature that have used the FIM to assess outcome form rehabilitation in patients with inflammatory peripheral neurological disorders. A few such studies exist, although most of them are single centre studies focussing patients with GBS Prasad et al reported outcomes from in-patient rehabilitation in 28 GBS patients using the FIM. They showed that activities dependent on the use of the legs  were the most severely affected, following by activities dependent on the use of arms (eg dressing), or use of both arms and legs (eg transfers) Khan et al reported a good functional recovery for GBS survivors residing in the community Novak at al reported outcomes from rehabilitation for patients with CIN The authors recognise a number of limitations to this study:Although the study population represents a national cohort of patients drawn from 45 different rehabilitation units across England, and is larger than the other published studies in the literature, the sample size is still relative small, particularly for the subgroups of CIN and CIDP.As with any cohort design that describes functional gain in rehabilitation, spontaneous improvement during the follow-up period cannot be excluded and it might suggest that part of the functional change is not the direct result of rehabilitationThe data were extracted from the UKROC database during a relatively early stage in its development when data reporting was not mandatory. Therefore, FIM+FAM data were available for only just over half of the total eligible population and NPDS data were available in approximately one-third. This could have led to some selection bias. From April 2013, the dataset was mandated as a requirement for commissioning of specialised rehabilitation services and routine benchmarking data on quality and cost-efficiency should come on stream from 2014, so future analyses of the dataset in 2–3 years’ time should provide a more comprehensive picture.The UKROC method of reporting cost-efficiency in terms of the time to offset the costs of rehabilitation is as yet specific to the UK. As noted in the Our methods for evaluating cost-efficiency are therefore not yet at a stage of development whether they could replace FIM-efficiency, but as this and other studies show, they have the potential to overcome some of the floor and ceiling effects that limit the usefulness of FIM-efficiency as a measure of value-based health care in rehabilitation.In conclusion, this study builds on the existing literature provides further evidence that patients with polyneuropathies have both physical and cognitive disabilities that are amenable to change with rehabilitation. In addition, it demonstrates significant reduction in on-going care-costs, especially for highly dependent patients, and provides new evidence for the cost-efficiency of rehabilitation in this group of patients although controlled study designs are required to identify the extent to which these outcomes reflect the added value of rehabilitation over and above natural recovery. Further research is also needed to explore the underlying reason for the diagnosis-specific differences between the various sub-groups. The study also highlights the need for clinicians to be aware that, even in the absence of overt cognitive impairment, patients with inflammatory polyneuropathies may have significant cognitive, psychosocial and communicative disabilities that need to be addressed in their own right as part of the rehabilitation programme.Patients with polyneuropathies have both physical and cognitive disabilities that are amenable to change with rehabilitation.The significant reduction in on-going care-costs, especially for highly dependent patients, supports new evidence for the cost-efficiency of rehabilitation in this group of patients.


Recent evidence suggests that these a priori boundaries often fail to adequately account for variation in rates and patterns of evolution among sites. Furthermore, new phylogenomic datasets such as those assembled from ultra-conserved elements lack obvious structural features on which to define a priori partitioning schemes. The upshot is that, for many phylogenetic datasets, partitioned models of molecular evolution may be inadequate, thus limiting the accuracy of downstream phylogenetic analyses.Model selection is a vital part of most phylogenetic analyses, and accounting for the heterogeneity in evolutionary patterns across sites is particularly important. Mixture models and partitioning are commonly used to account for this variation, and partitioning is the most popular approach. Most current partitioning methods require some We present a new algorithm that automatically selects a partitioning scheme via the iterative division of the alignment into subsets of similar sites based on their rates of evolution. We compare this method to existing approaches using a wide range of empirical datasets, and show that it consistently leads to large increases in the fit of partitioned models of molecular evolution when measured using AICc and BIC scores. In doing so, we demonstrate that some related approaches to solving this problem may have been associated with a small but important bias.Our method provides an alternative to traditional approaches to partitioning, such as dividing alignments by gene and codon position. Because our method is data-driven, it can be used to estimate partitioned models for all types of alignments, including those that are not amenable to traditional approaches to partitioning. The accuracy of phylogenetic inference often relies on the use of an appropriate model of molecular evolution ,2. InaccAccounting for the heterogeneity in the rates and patterns of evolution among sites in a DNA sequence alignment is an important part of selecting a model of molecular evolution -15. Amon931 possible partitioning schemes: well beyond anything that can be feasibly analyzed by brute force. A related approach is to allow the data inform the assignment of sites to subsets, and to integrate out the uncertainty in these assignments in a Bayesian framework [An inherent obstacle in partitioned phylogenetic analyses is the choice of an appropriate partitioning scheme. One approach would be to evaluate every possible partitioning scheme for a given dataset and choose the best scheme, perhaps according to one of the commonly used information theoretic metrics such as the AICc  or BIC  or by soramework . AlthougThe most commonly used method for partitioning alignments, and the only one currently suited to very large datasets, is to define subsets according to structural features of the sequences in the alignment, such as gene boundaries, codon positions, structural components of rRNAs (such as stems and loops), or some combination of these. We call this ‘traditional’ partitioning throughout this manuscript. This approach is also known as mechanistic modeling because it describes known biological or mechanistic processes and is motivated by the observation that different molecular features can have different patterns of molecular evolution ,21,36-43A number of recent studies have suggested that traditional approaches to partitioning can be inadequate. Evidence suggests that there can be substantial heterogeneity of the evolutionary process within a single codon position of a single gene ,44-48 ana priori. Thus, we face the problem that we lack adequate ways to model molecular evolution for some of the largest and most promising empirical datasets in our field.Another limitation of traditional partitioning involves its application to new types of molecular markers. Many of the latest methods for assembling phylogenomic datasets result in large alignments that consist either entirely or largely of non-protein coding DNA (e.g. introns and ultra-conserved elements (UCEs)) -54. It cOne approach to choosing a partitioning scheme for large datasets is to group sites into subsets using estimates of site rates ,55-57. KIn this study, we develop a new algorithm that automatically defines partitioning schemes by clustering similar sites together into subsets. Our approach improves on previous work in three important ways. First, while previous approaches ,55-58 haWe demonstrate our approach on a wide range of datasets. Our results show that our method can be used to select partitioning schemes for the full range of datasets used in phylogenetics: from small barcoding datasets to large phylogenomic datasets consisting of ultra-conserved elements. In all cases, our method finds partitioning schemes that outperform those selected with traditional approaches to partitioning, when measured by metrics such as AICc and BIC.We follow the terminology established in other studies on partitioning ,32,33 whEstimate a starting tree topology from the multiple sequence alignment;Start with a partitioning scheme that has all sites assigned to a single subset, and choose the best-fit substitution model for that subset;Calculate the information theoretic score of the current partitioning scheme;Generate site rates for the focal subset;k-means clustering;Divide the focal subset into two subsets using Choose the best-fit substitution model for each of the two new subsets;Calculate the information-theoretic score of the partitioning scheme in which the two new subsets from 4c replace the focal subset;If the information theoretic score improves, label the focal subset for division.For each subset in the current partitioning scheme, test whether that subset should be further divided:If no subsets have been labeled for division, terminate the algorithm. Otherwise, define a new partitioning scheme in which each labeled subset in the list is replaced by the two correspondingly smaller subsets defined in step 4b and return to step 4.We present an algorithm Figure  that autIn step 1, we estimate a tree topology with branch lengths for the dataset. Optimizing the tree topology at each step would be computationally intensive, particularly for large phylogenomic datasets. For this reason, we use a fixed tree topology throughout the course of the algorithm. In principle, any method to estimate a starting tree could be used since it has been argued that a non-random tree is likely to be sufficient for model selection -61; in oIn step 2, we define a partitioning scheme in which all sites in the alignment are assigned to a single subset, and we then select a best-fit model of molecular evolution for this subset. The model selection step uses an information theoretic metric (e.g. the AICc or the BIC) to choose a substitution model from a list of candidate models. Here we select the best model from the set of 56 submodels of the GTR model available in PartitionFinder v1.1.1 . These iIn step 3 we calculate one of two information theoretic scores (the AICc and the BIC ) for theP(i) = {{1, 2, 6}, {3}, {4, 5}}. P(i)  consists of a set of at most four sets, that contain the sequence numbers in the alignment pattern that have, respectively, the nucleotides A, C, G, or T at site i. The number of non empty sets, which we denote by |P(i)| is equal to the number of different nucleotides found in site pattern i. The character partition of each site is then compared to the character partition of every other site. The sites are evaluated for agreement with every other site using a “partition agreement score”, ), which is defined as:In step 4, we decide whether to subdivide each of the subsets in the current partitioning scheme. In step 4a, we fit a GTR + G model of molecular evolution to the subset, conditioned on the tree and its relative branch lengths estimated during step 1 using maximum likelihood in PhyML . Then wea) is equal to 0 or 1 depending on whether x is compatible with the character partition of site i, i.e. if x is a subset of one of the sets in P(i):where ri) for the alignment pattern at site i is then obtained by computing the mean partition agreement score across all sites:The rate , while the sites that are less similar to the pool of sites in the alignment are considered fast with rates approaching 0. It is important to note that this method does not take into account the character state in an alignment pattern when the set partitions are compared, e.g. the set partition and resulting site rate of “AACGGA” would be identical to that of “TTGAAT”. Although software exists to calculate TIGER site rates , we founk-means clustering algorithm to divide the sites in the focal subset into two clusters based on one or more of the site-wise parameter estimates from step 4a. K-means is a fast clustering algorithm capable of handling large datasets with high dimensionality [k-means is to minimize the function:In step 4b, we use the ionality ,64. It ck is the number of clusters, μ is the cluster centroid, and x is any given data point, in the case of this study, the site rates, and ‖x‖ is the L2 norm, or Euclidean length, of x. The algorithm proceeds through two steps:The assignment step, in which each point is assigned to a cluster with its closest centroid.The update step, in which cluster centroids are moved to the center (mean) of their new clusters.Where k) is chosen a priori and fixed at 2 in our case, and then k centroids are placed within the sample space. The initial placement of centroids is an important step; poor placement can result in an unsatisfactory exploration of the sample space and, although the algorithm may converge, it may only reach a local optimum. To avoid this, we use the k-means++ centroid initialization method, which has been shown to be superior when compared to other centroid seeding techniques such as random placement [k-means algorithm, selecting the initialization that best minimizes the within-cluster sum of squares. Following initialization, Euclidean distances between each data point and the centroids are calculated and each data point is assigned to a cluster based on its nearest centroid. The centroids are then moved to the mean of their respective clusters (the k-mean) and distances are recalculated. This process is repeated until the centroids no longer move beyond a threshold at the end of the iteration. We used the k-means algorithm from the scikit-learn package implemented in Python [The number of clusters  estimate the best model for each of the two new subsets from our set of candidate models as described above. We then (step 4d) calculate the information-theoretic score of two partitioning schemes: one in which the focal subset is retained as a single subset, and one in which the focal subset is divided into two new subsets. If the overall information theoretic score of the latter partitioning scheme is better, we label the focal subset as one that should be divided.In steps 4c and 4d, we use the output of the Once step 4 has been applied to all of the subsets in the current partitioning scheme, we ask whether there are any subset divisions that improved the overall information theoretic score (step 5). If there are none, then the algorithm terminates, since we are unable to find a partitioning scheme better than the current scheme. Otherwise, we divide all of the subsets that are labeled for division in step 4. Then the algorithm iterates.The algorithm above makes the assumption that likelihoods can be calculated for any collection of sites in an alignment. During the development of the algorithm, we found some cases in which PhyML was unable to analyze some subsets. This was usually because the alignments were too small or contained only sites with identical site patterns. Since our aim is to produce partitioning schemes that can be used to estimate phylogenetic trees with programs like PhyML, and since these problematic subsets are likely to occur during any approach similar to the one we describe here, we designed the following solution. First, we flag the problematic subsets as the algorithm proceeds, and make the conservative assumption that their site-likelihoods will be identical to their site-likelihoods in the larger subset from which they were generated. This allows us to estimate conservative information theoretic scores for partitioning schemes as the algorithm proceeds. At the end of the algorithm (i.e. after step 5), we combine each of the problematic subsets with their nearest neighbor subset, defined as the non-problematic subset with the centroid (estimated in step 3a) that has the shortest Euclidean distance to the centroid of the problematic subset. This process is repeated until there are no problematic subsets, i.e. until PhyML can successfully analyze all of the subsets in the partitioning scheme.k-means algorithm, we compared ten partitioning scheme selection approaches on ten different datasets  iterative k-means with TIGER site-rates.To evaluate the performance of the iterative ts Table . The appts Table . The fivDuring the empirical evaluation, one dataset, McCormack 2013 , was took-means method we develop here. Specifically, we were unsure whether site rates calculated under the assumption that the starting tree is true would bias our partitioning schemes toward recovering the starting tree during downstream phylogenetic analyses. Thus, we designed a simple test to evaluate starting tree bias.Although it has been shown that a starting tree topology is unlikely to negatively affect model selection as long as it the starting tree is non-random , it was k-means with likelihood site-rates, and iterative k-means with TIGER site rates . Fourth, we estimated a maximum-likelihood phylogenetic tree using RAxML for all 60 partitioning schemes for each dataset. For each of the three methods, the process resulted in a collection of 20 distinct starting trees, and 20 estimated ML trees. The final step in the process involved statistically testing whether the starting trees are more similar to their corresponding ML trees than would be expected by chance. To do this, we used a bootstrap test in which the observed test statistic is the sum of the Robinson-Foulds [To test whether the starting tree introduced bias into the estimation of the partitioning scheme, we used a five-step process. First, we estimated a neighbor-joining (NJ) tree for the data. Second, we created twenty new trees, where each new tree was a single subtree-prune and regraft (SPR) move away from the NJ tree, giving a set of 20 plausible non-random trees for the dataset. Third, we used these 20 trees as starting trees from which we estimated 20 partitioning schemes for each dataset using three methods: the PartitionFinder greedy algorithm, iterative n-Foulds  distancek-means algorithm on empirical datasets, we also evaluated our method with a simple simulation. First, we simulated a tree under the Yule (pure-birth) process in INDELible v1.03 [k-means with TIGER rates. Each step, from tree simulation through partitioning scheme selection was repeated 20 times. These conditions were chosen to explicitly test whether the iterative k-means algorithm would 1) assign alignment sites to subsets containing other sites generated from the same model, and 2) find the correct number of subsets.While this paper was primarily aimed at evaluating the efficacy of the iterative le v1.03 . We chosle v1.03  model. Nk-means algorithm and evaluate its performance on empirical data as compared to other commonly used partitioning strategies. To do this, we selected partitioning schemes for published empirical datasets using several different methods and compared the relative fit of each partitioning scheme using AICc and BIC.The primary purpose of this study was to describe the iterative k-means algorithm substantially outperformed all other partitioning approaches for each of the ten datasets we analyzed, regardless of the details of the k-means approach or the information theoretic metric we used . When tet Table , Table 2k-means clustering also worked well for datasets consisting of protein coding genes from the standard phylogenetic toolbox. A close examination of the partitioning schemes reveals that the algorithm chooses subsets that reflect the traditional biological partitioning boundaries such as genes and codon positions  required the additional parameters of gamma, proportion of invariable sites, or both. In contrast, of the 92 subsets chosen with the PartitionFinder greedy algorithm, 86 (93.5%) of the models included gamma, proportion of invariable sites, or both. These results support recent observations that more flexible models of variation in rates among sites tend to fit the data much better than those that rely on distributional assumptions [k-means approach to partitioning may be particularly useful when the variation in rates across sites cannot be adequately modeled using a combination of traditional partitioning (e.g. using genes and codon positions) and gamma-distributed rates [The iterative k-means algorithm provides a useful data-driven method to account for complex patterns of variation in rates of molecular evolution among sites. This is primarily because it tends to group together sites that evolve at similar rates of evolution, reducing the need for additional parameters to describe variation in rates across sites within a given group of sites Figure . For exaumptions ,77, and ed rates ,77. Otheed rates ,48,78-80ed rates  that hased rates . Our metk-means with TIGER rates for the simulated alignments based on the criteria that, 1) alignment sites generated under the same model would be assigned to the same subsets, and 2) the correct number of subsets would be chosen. Our results show that most subsets consisted primarily of sites generated from the same model , and often many fewer. For example, for the McCormack et al. dataset [k-means method selects partitioning schemes that make biological sense with respect to what we already know about variation in rates and patterns of evolution , we also showed how these pitfalls could be overcome. These methods provide an important step forward in improving our approaches to modeling molecular evolution, particularly for very large datasets, as well as suggesting fruitful directions for further improvements.Partitioning remains the most commonly used method for accounting for variation in the rates and patterns of molecular evolution among sites in phylogenetic analyses. As the size and number of phylogenomic datasets grows, it is increasingly important to fit more realistic partitioned models to those datasets. The algorithm we present in this paper does this by automatically selecting a partitioning scheme for datasets of variable size and type without the need of an http://dx.doi.org/10.6084/m9.figshare.1274798). The modified PartitionFinder code we developed for this manuscript is available at https://github.com/brettc/partitionfinder/tree/paul_develop and the TIGER rates code that we developed is available at http://dx.doi.org/10.5281/zenodo.12914. The data sets that were analyzed during empirical evaluation can be found at https://github.com/roblanf/PartitionedAlignments.All code, alignments, configuration input files, and output files have been uploaded to FigShare (


Accumulating evidence indicates a strong correlation between allergic disease and cardiovascular risks. In spite of this, the data concerning the association between allergic rhinitis (AR) and cardiovascular risks is sparse and conflicting. This study aimed to investigate the association between AR prevalence and metabolic syndrome (MetS) in a large-scale, population-based survey, while considering the relevant risk factors.A nationwide cross-sectional study was conducted based on data from 30,590 subjects aged 19 years and older, from the Korean National Health and Nutrition Survey 2007–2013. The odds ratios (ORs) and 95 % confidence intervals (CIs) of AR prevalence, based on MetS status and the presence of any MetS component, were calculated using multiple logistic regression analyses.Regarding the characteristics of patients with AR and/or MetS, some variables had significant associations with disease in inverse directions for AR and MetS. Multivariate logistic analysis, with adjustments for demographic variables and health habits, indicated that AR prevalence was significantly lower in subjects with MetS , high blood pressure , or impaired fasting glucose . Furthermore, high blood pressure and impaired fasting glucose were significant predictors for reduced AR prevalence, independently of other MetS components.In this population, AR was diagnosed less frequently in subjects with metabolic conditions. Well-designed prospective studies allowing for medical service utilization and collaborative basic research are warranted to elucidate the mechanism responsible for this inverse relationship. Extensive studies have revealed that asthma patients are at greater risk of cardiovascular disease (CVD), with multiple mechanisms proposed . AllergiThe differing age of onset of AR and CVD, may complicate the relationship between them. The prevalence of AR is highest in the second to fourth decades of life, and then gradually diminishes; this is in contrast to the prevalence of CVD, which increases later in life . It is aThis study was based on data obtained from the Korean National Health and Nutrition Examination Survey 2007–2013, which was a nationwide population-based survey conducted by the Korean Ministry of Health and Welfare. Participants were randomly selected through a stratified, multistage, probability-sampling design according to sampling units based on age group from household registries as well as economic status, sex, and geographic area . This stn = 1192), other allergic diseases  and those who had no available data on the two main variables  were excluded. Thus, a dataset of 30,590 patients was used in the final analysis.We identified 44,118 participants of 19 years of age or older. Of these, subjects who were diagnosed with overt CVD , health behaviors , and history of disease as diagnosed by a physician  was collected during the health interview. Waist circumference was measured at the narrowest point between the lower border of the rib cage and the iliac crest. BP was measured from the right arm after the subject had rested for 5 min in a sitting position using a standard mercury sphygmomanometer . Blood samples were obtained after a 12-h overnight fast, and fasting plasma glucose, triglyceride, and high-density lipoprotein cholesterol levels were measured using a Hitachi 700-110 Chemistry Analyzer .The definition for MetS and its components was based on the National Cholesterol Education Program Adult Treatment Panel III guidelines, and we used the ethnicity-specific values for waist circumference based on data from the Korean Society for the Study of Obesity . MetS waEconomic status was classified into “low” or “high” according to family size-adjusted mean monthly household income, which was calculated by dividing household income by the square root of the number of persons in the household. The region in which the subject lived was categorized as either urban or rural. The capital city, its surrounding metropolitan area, and other metropolitan cities were grouped as urban areas; other areas were considered rural. Educational level was categorized as “middle school or lower” and “high school or beyond”. Marital status was classified as “married” or “unmarried”; “unmarried” included “single” and “divorced/separated/widowed”. Occupations were reorganized into three groups: “unemployed”, “white-collar” or “blue-collar”.Physical activity was categorized as “regular physical activity” when participants engaged in moderate intensity activity more than five times per week, or vigorous activity more than three times per week, or “other”. The subject was considered a “never-smoker” if the subject had never smoked a cigarette, or otherwise an “ever-smoker.” For alcohol consumption, subjects were considered “frequent drinkers” if they consumed more than seven drinks (men) or five drinks (women) in one sitting more than 2 days per week .P-values less than 0.05 were considered statistically significant.All analyses were performed using the STATA SE 9.2 . Descriptive statistics were presented as percentages. Differences in characteristics depending on AR or MetS status were evaluated using a Chi square test. Odds ratios (ORs) of AR prevalence in subjects with metabolic conditions and their 95 % confidence intervals (CIs) were calculated using both unadjusted and adjusted logistic regression models. All statistical tests were two-tailed, and results with Table The ORs for AR prevalence in subjects with MetS and its components are shown in Table As the mechanisms behind our findings remain unclear, our findings should be interpreted with caution. To the best of our knowledge, there is no evidence supporting the preventive effect of AR for CVD risks. Conversely, a growing body of evidence suggests that inflammatory mediators and cells involved in AR may be implicated in the process of atherosclerosis , 15. CliIt is reasonable to interpret our results to signify a decreased prevalence of AR in subjects with metabolic conditions. Medical service utilization by socioeconomic status may explain these findings . As indiAdditionally, the helper T cell paradigm suggests that type 1 and type 2 helper T cells reciprocally counteract each other , which mThis study has several limitations. First, we relied on self-reporting of AR and also did not differentiate between AR subtypes. Second, the cross-sectional design does not permit us to infer causality between AR prevalence and CVD components. To determine causality, a clinical trial in which the incidence of AR between two groups  is compared over time, would be required.The present study is the largest to date to examine the relationship between AR prevalence and CVD risks. Our results underscore a significantly lower prevalence of AR in subjects with an identified metabolic dysfunction. Additional experimental and in-depth prospective studies are required to elucidate the mechanisms linking AR and CVD.


That most epithelium remains AR positive, but ligand independent, suggests the presence of oncogenic AR variants. To address this hypothesis, we have used a panel of recently described -/-Pten tumor cell lines derived from both from hormone intact  and castrated Pten mutants  followed by RACE PCR to identify and characterize three novel truncated, amino terminus containing AR variants . Variants appear not only conserved throughout progression but are correlated with nearly complete loss of full length AR (AR-FL) at castrate androgen levels. The overexpression of variants leads to enhanced transcriptional activity of AR while knock down studies show reduced transcriptional output. Collectively, the identification of truncated AR variants in the conditional PTEN deletion model supports a role for maintaining the CRPC phenotype and provides further therapeutic applications of this preclinical model.Androgen receptor (AR) variants are associated with resistance to anti androgen therapy both in human prostate cancer cell lines and clinical samples. These observations support the hypothesis that AR isoform accumulation is a consequence of selective therapeutic pressure on the full length AR. The Pten deficient prostate cancer model proceeds with well-defined kinetics including progression to castration resistant prostate cancer (CRPC). While surgical castration and enzalutamide treatments yield an initial therapeutic response,  These AR variants all contain the AR-NTD with only one containing a partial sequence overlap with the AR-LBD. The relative expression of these variants not only increases in response to castration but in response to AR targeted therapy including casodex and enzalutamide. Remarkably, after progression at long term of castrate androgen levels, we observed a significant reduction in full length AR but maintenance of the identified isoforms. Functional studies revealed that overexpression or knock down of AR-Va and AR-Vc could regulate AR transcriptional output.Collectively, our data support the presence of new AR variants that may serve as important contributors during progression to CRPC. Variant identification also significantly enhances the utility of the PTEN null prostate cancer model both in terms of understanding the function of AR and as a system for assessing therapies targeting resistant forms of the androgen receptor.Pten null mutant mice. Cell lines were characterized as being Pten-/-, having activated PI3K/Akt signaling and features of the primary tumor model . To. ToPten or model  and humal  [18 [1817] . One of  therapy  35]  and and26] aTotal cellular RNAs were isolated from E8 and cE1 by RNAqueous-4PCR Kit (Life technologies) used as the source. The detection of mouse AR splice variants from them was achieved by GeneRacer Kit with SuperScript III RT and TOPO TA Cloning Kit for Sequencing (life technologies) as per manufacture’s protocols. Briefly, extracted RNAs were first reverse transcribed using the GeneRacer oligo (dT) primer and SuperScript III RT module from the kit. RACE-ready cDNAs were subjected to the 3’ rapid amplification of cDNA end PCR (3’ RACE-PCR) using a forward gene-specific primer (GSP) and reverse GeneRacer 3’ primer from the GeneRacer Module. Another round of nested PCR was conducted for further amplification using a forward GSP nested primer and reverse GeneRacer 3’ Nested primer provided in the kit. SuperTaq Plus Polymerase (Life technologies) was used for both 3’RACE and nested PCR. Forward GSP and nested primers anchored within exon1  for predPlasmid constructs used in this study included: Rat AR full-length , human AR full-length , and PSA-luciferase reporter and PCDNA3.1-myc . pCR4-TOPO was purchased in the TOPO TA Cloning Kit for Sequencing (life technologies).CDS (coding sequences) for mARVa, Vb and Vc were amplified by PCR from cDNAs of E8 and cE1 using the primer sets enclosing the entire open reading frame (ORF), and sub cloned into pcDNA3.1-myc-HisC. To generate the ARVa-myc and ARVc-myc constructs, reverse primers were re-designed to be anchored upstream of their own stop codons and in-framed with the ORF of expression vector. Hifi AccuStart Taq DNA Polymerase (Quanta Biosciences) and HotStar Taq DNA Polymerase (Qiagen) were applied in PCR cloning. Sequences of primer used to clone Va, Vb, Vc, Va-myc and Vc-myc were provided in Total cellular RNAs were extracted from all parental and other derivative cell lines and then reverse transcribed using oligo (dT) primer and SuperScript III First-Strand Synthesis (Life technologies). RNA samples used for various assays were isolated from cells with different batches. Conventional PCR was performed on cDNAs using the same forward primer anchored within exon1 paired with reverse primer specific for mAR-Va, b, c and AR-FL, respectively . Quantit1–2 days prior to transfection, cells were placed in media containing charcoal-stripped serum (CSS). For all transfections, pools of cells were transfected using Lipofectamine Plus (Invitrogen) with empty vector control plasmid, full-length AR plasmid, or AR splice variant plasmids along with the androgen-responsive constructs MMTV-firefly luciferase or PSA-firefly luciferase  and the COS cells were transfected with mARVa-myc or mARVc-myc together with Rat AR-FL using Lipofectamine 2000 (Life technologies) as per the manufacture’s protocol. About 48 hrs. post-transfection cells were lysed with 1% CHAPS lysis buffer . Then 500 μl cleared cell lysate were immunoprecipitated by incubation with 5 μg anti-myc mAb (Roche) and Protein G PLUS-Agarose (EMD Millipore) overnight at 4°C. The isotype matched control mAbs were included in all immunoprecipitation experiments as negative control. Immunoprecipitates were washed twice in 0.1% lysis buffer followed by elution separation on 4–20% Criterion Tris-HCl Gel (Bio-Rad) under reduced conditions.Whole-cell lysates from cell lines and primary tissues were prepared as described previously . Proteinad libitum. Breeding and experimental procedures were performed according to mouse protocols approved by the Institutional Animal Use and Care Committee (IACUC) at the University of Southern California and Icahn School of Medicine, Mount Sinai. The current study is specifically approved under the protocol: “The Function of the PTEN tumor suppressor gene in tumorgenesis and animal development” with Lab Animal Protocol Reference #LA13-00060. Conditional Pten-null mutant mice  and Pten-null;Ar-null mutant mice were carried on a pure C67/BL6 background Only male mice were used for experimental procedures.All mice were maintained in a temperature and humidity controlled, pathogen free housing units, with light-dark cycles  allowing food and water distribution Surgical castration recovery surgery was carried out in Pten mutant mice while under full anesthetic using Ketamine (50 mg/ml) and Xylazine (20 mg/ml) according to an approved animal protocol (Reference #LA13-00060).S1 FigSequences were obtained through 3’ RACE PCR on cDNA generated from murine prostate cancer cell lines . RACE PCR was followed by nested PCR to identify new AR variants.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigC+;PtenL/L, n = 5) were treated for >20 wks with enzalutamide and then assessed for AR expression using antibodies against the animo terminus  or carboxyl terminus . Significant nuclear expression is observed with the N-20 antibody but only low expression with the C-19.Pten mutants ((PDF)Click here for additional data file.S4 FigbVariant dependent PSA-luc transactivation   .Presence of AR-FL is required for transactivation of the PSA-luc reporter  . (PDF)Click here for additional data file.S5 FigC+;PtenL/L, 6 wks) and assessed at 30 wks for AR  and FRKBP5 expression . Hormone intact Pten-null;Ar-null  mutants assessed at 30 wks for AR  and FRKBP5 expression . CRPC Pten-null cell lines derived from the Pten-null mouse model assessed for Fkbp5 gene expression in hormone intact (FBS), castrate (CSS) or with exogenous androgen (1 nM R1881) over 20 hours treatment .a) Pten null mutants were castrated ((PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


Acute kidney injury (AKI) is common in critically ill patients. AKI requires renal replacement therapy (RRT) in up to 10% of patients. Particularly during connection and fluid removal, RRT frequently impairs haemodyamics which impedes recovery from AKI. Therefore, “acute” connection with prefilled tubing and prolonged periods of RRT including sustained low efficiency dialysis (SLED) has been suggested. Furthermore, advanced haemodynamic monitoring using trans-pulmonary thermodilution (TPTD) and pulse contour analysis (PCA) might help to define appropriate fluid removal goals.Since data on TPTD to guide RRT are scarce, we investigated the capabilities of TPTD- and PCA-derived parameters to predict feasibility of fluid removal in 51 SLED-sessions  in 32 patients with PiCCO-monitoring . Furthermore, we sought to validate the reliability of TPTD during RRT and investigated the impact of “acute” connection and of disconnection with re-transfusion on haemodynamics. TPTDs were performed immediately before and after connection as well as disconnection.Comparison of cardiac index derived from TPTD (CItd) and PCA (CIpc) before, during and after RRT did not give hints for confounding of TPTD by ongoing RRT. Connection to RRT did not result in relevant changes in haemodynamic parameters including CItd. However, disconnection with re-transfusion of the tubing volume resulted in significant increases in CItd, CIpc, CVP, global end-diastolic volume index GEDVI and cardiac power index CPI. Feasibility of the pre-defined ultrafiltration goal without increasing catecholamines by >10% (primary endpoint) was significantly predicted by baseline CPI  and CItd .TPTD is feasible during SLED. “Acute” connection does not substantially impair haemodynamics. Disconnection with re-transfusion increases preload, CI and CPI. The extent of these changes might be used as a “post-RRT volume change” to guide fluid removal during subsequent RRTs. CPI is the most useful marker to guide fluid removal by SLED. Acute kidney injury (AKI) occurs in up to 30% of critically ill patients, requires renal replacement therapy (RRT) in up to 10% and markedly increases mortality . FurtherIf RRT is required due to AKI, similar risks and side effects have to be considered as during chronic renal replacement therapy (CRRT). These risks include the need for anticoagulation, intra-dialytic hypotension as well as difficulties in defining and reaching dry weight . HoweverAnother approach is optimization of hemodynamics based on modern haemodynamic monitoring. The most recent guideline suggest using protocol-based management of haemodynamics and oxygenation parameters to prevent development or worsening of AKI in high-risk patients in the perioperative setting or in patients with septic shock .Prediction of feasibility of fluid removal has been investigated in CRRT . In addiDepending on the underlying disease, a substantial number of critical care patients are equipped with advanced hemodynamic monitoring including pulse contour analysis (PCA) and trans-pulmonary thermodilution (TPTD). Both principles are combined in at least two commercially available devices . TPTD is used for indicator dilution to measure the static preload marker global end-diastolic volume index GEDVI –15, the fluid responsiveness there is an amazing lack of data on fluid removability by RRT.These parameters have been investigated extensively regarding prediction of “fluid responsiveness”, which is defined as a substantial increase in CI in response to a volume challenge. Moreover, these parameters might be useful to predict the feasibility and extent of fluid removal by RRT (“fluid removability”). Compared to the plethora of data on This might be related to concerns on the applicability of indicator dilution techniques including TPTD during RRT. From a theoretical viewpoint several potential confounders of thermodilution have to be considered  includinon) and without (T2off) blood flow induced by the blood pump as well as before disconnection with (T3on) and without (T3off) blood pump, and finally after disconnection (T4).To investigate potential confounding and impact of the RRT itself, haemodynamic measurements can be analyzed at a minimum of 6 different times ; Fig 2: The number of potential study protocols can be multiplied by also considering different techniques of RRT F) and the modality of connection which can be performed in an “acute” modality with pre-filled tubing and in a “chronic” RRT modality without pre-filling.on vs. T2off and T3on vs. T3off) and the overall impact of RRT on haemodynamics (T4 vs T1)  divided by 451 [The cardiac power index CPI was calculated as the product of mean arterial pressure [mmHg] and cardiac index [L/min/md by 451 .on”and “T3on“.Measurements at T2 and T3 during RRT were performed without reducing the flow of blood and dialysate. Therefore, in the context of this study we used “T2” and “T3” instead of “T2SLED was performed using the GENIUS 90 device  with FX40, FX50 or FX60 dialyzers containing 32 mL, 53 mL and 74 mL, respectively. Dialyzers were connected with GENIUS 90 XS tubing  with a volume of 121 mL. FX40, FX50 or FX60 dialyzers and tubing were pre-filled with a total of 153 mL, 174 mL or 195 mL of 0.9% saline, respectively. Blood and dialysate flow were set at 150 mL/min.For the venous access for RRT Gambro Gam Cath Dolphin-catheters were used. Catheters with a length of 250mm and a diameter of 13 F were used for femoral access and catheters with a length of 150–175 mm and a diameter of 13 F were used for jugular RRT-access, respectively. CVC and dialysis catheters were inserted in different positions (one in vena cava superior and the other one in vena cava inferior).In chronological order the following endpoints were investigated:Comparison of CIpc-T2 vs. CItd-T2 to analyze validity of CItd-T2 which could be confounded by loss of indicator during ongoing RRT, which does not apply to CIpc-T2 recorded immediately before TPTD-T2 .Comparison of changes in CItd vs. changes in CIpc before and after connection to RRT (CItd-T2 –CItd-T1 vs. CIpc-T2 –CIpc-T1) to analyze the validity of changes in CItd after initiation of RRT which could be confounded by loss of indicator during ongoing RRT, which does not apply to changes in CIpc.Comparisons of CItd-T2 to CItd-T1 and of CIpc-T2 to CItd-T1 to investigate the impact of acute connection to RRT on haemodynamics with pre-filled tubing.“iso-volaemic” connection to RRT on haemodynamics we compared parameters derived from TPTD  as well as MAP, CVP, systemic vascular resistance index (SVRI) and heart rate before and after connection to RRT (T2 vs. T1).To further investigate the impact of Comparison of CIpc-T4 vs. CItd-T4 to analyze validity of CItd-T4 which could be confounded by loss of indicator during ongoing RRT, which does not apply to CIpc-T4 recorded immediately before TPTD-T3.Comparison of changes in CItd vs. changes in CIpc before and after disconnection to RRT (CItd-T4 –CItd-T3 vs. CIpc-T4 –CItd-T3) to analyze validity of changes in CItd after termination of RRT which could be confounded by loss of indicator during ongoing RRT, which does not apply to changes in CIpc.hyper-volaemic” disconnection to RRT with re-transfusion of the blood within the tubing within 2–3 minutes  on haemodynamics with pre-filled tubing.Comparisons of CItd-T4 to CItd-T3 and of CIpc-T4 to CItd-T3 to investigate the impact of a”hyper-volaemic” disconnection to RRT on haemodynamics we compared parameters derived from TPTD  as well as MAP, CVP, SVRI and heart rate HR before and after disconnection to RRT (T4 vs. T3).To further investigate the impact of”primary endpoint was the analysis of the predictive capabilities of haemodynamic parameters regarding “fluid removability” which was defined as achieving ≥90% of the pre-defined net-filtration goal without an increase in the vasopressor dosage of more than 10% or initiation of a vasopressor. The net-filtration goal was defined by an investigator not directly involved in the study.The In a first step we analyzed predictive capabilities of baseline haemodynamic parameters including MAP, CVP, CI, SVI, GEDVI, EVLWI and CPI.In a second step we analyzed predictive capabilities of changes in continuously  and discontinuously measured parameters in course of RRT compared to baseline.To compare changes in continuous variables before and after connection to RRT we used Wilcoxon-test for paired samples. To compare continuous parameters between different groups achieving and failing the primary endpoint, the Wilcoxon-test for unpaired samples was used.Multivariate binary regression analysis regarding the primary endpoint was performed including variables which were significantly different between patients achieving or failing the primary endpoint or with a p-value <0.2 in univariate analysis.Receiver operating characteristics (ROC)-analyses were performed to assess discriminative ability of univariate predictor variables regarding the primary endpoint.In a limited number of TPTD measurements, single haemodynamic variables could not be obtained. Therefore, statistical tests were calculated based on the measurements with valid data.All statistical tests were performed on a two-sided level of significance of α = 5% using IBM SPSS Statistics 23 .Hypothesizing that TPTD, but not PCA might be confounded by the RRT-blood pump we compared CItd during RRT to CIpc recorded immediately after connection to RRT (CItd-T2 vs. CIpc-T2) and after disconnection connection from RRT (CItd-T4 vs. CIpc-T4).2; p = 0.983). Furthermore, changes in CItd (CItd-T2 –CItd-T1) and changes in CIpc (CIpc-T2 –CItd-T1) after connection were not significantly different .CItd-T2 was not significantly different to CIpc-T2 . Similarly, changes in CItd (CItd-T4 –CItd-T3) after disconnection were slightly higher than changes in CIpc . Regarding the small differences and borderline levels of significance, this can be considered as clinically not relevant.After disconnection CItd-T4 was slightly different to CIpc-T4  was fulfilled. In 18 of 51 RRTs (35%) less than 90% of the pre-set goal could be filtrated, in 16 of 51 RRTs the catecholamine dosage had to be increased. In five RRTs less than 90% of the filtration goal could be achieved despite increase in the catecholamine dosage.2; p = 0.016) and CI (4.98±1.34 vs. 4.07±1.48 L/min/m2) compared to patients not fulfilling the primary endpoint.Patients fulfilling the primary endpoint had significantly higher baseline values of CPI .In multivariate analysis also including baseline values of CPI, CVP, GEDVI, CI, SVI, MAP, S2 for CPI had a sensitivity of 77% and a specificity of 55% regarding the primary endpoint.The ROC-AUC regarding the primary endpoint was 0.712  for baseline CPI and 0.662  for baseline CI . A cut-o2 in patients fulfilling the primary endpoint compared to -0.0431±0.18 W/m2 (p = 0.029) in those not fulfilling the primary endpoint.Among changes in continuously reported haemodynamic parameters after connection and before disconnection only changes in CPI compared to CPI after connection were significantly associated to the primary endpoint: The difference between PCA-derived CPI before disconnection and CPI after connection (CPIpc-T3 –CPItd-T2) was 0.07±0.18 W/m2; p = 0.012) and heart rate , but a moderate increase in MAP  and noradrenalin dosage .Comparison of values after disconnection to values before connection demonstrates slight decreases in CItd , GEDVI, EVLWI and dPmax were not changed by RRT .AKI requiring RRT is associated with impaired long-term outcome and mortality. Therefore, avoidance of AKI and limitation of side effects of RRT are major goals of intensive care. RRT itself is associated with potential risks including complications of anti-coagulation and cardiovascular side effects which can also impair recovery of AKI. Modern haemodynamic monitoring and optimization of fluid balance hold the potential of reducing the risks of AKI and RRT.Nevertheless, there is a lack of data on the use of TPTD and PCA in patients with AKI and RRT. This might, in part, be related to concerns about confounding of TPTD-results by RRT itself. While numerous studies investigate prediction of fluid responsiveness, there is scarce data on the use of TPTD and PCA to predict feasibility of fluid removal.Therefore, our study was elaborately designed to investigate potential confounding of TPTD by RRT, the impact of connection and disconnection of SLED on haemodynamics and finally the prediction of “fluid removability” in ICU patients with RRT.The main results can be summarized as follows:2 and a percentage error of 14.7% would be in line with an “interchangeability” of CItd and CIpc.Comparison of absolute values and changes in CItd to CIpc before and after (dis)connection did not give any hints for a substantial impairment of TPTD by SLED. Although CItd-T4 and CIpc-T4 were significantly different, the bias of -0.097±0.31 L/min/mConnection to SLED in an “acute modality” with pre-filled tubing did not result in haemodynamic instabilities or in any significant changes of haemodynamic parameters derived from TPTD, PCA or routine measurements at all.By contrast, disconnection with re-transfusion of the blood contained in the tubing induced an increase in preload, cardiac output and MAP. Therefore, re-transfusion can be considered as a kind of volume challenge at the end of SLED and filtration.Finally, our data demonstrate that higher baseline values of CPI and CI are predictive for achieving pre-set goals for ultrafiltration without substantial increase in the vasopressor dosage.With regard to the trial design, in a first step we investigated the validity of CItd during ongoing RRT. Our results give no hints of confounding of CItd by low blood-flow RRT which is in line with the majority of the limited data available ,22,23. TComparison of CItd (and other parameters derived from TPTD) to CItd with pump on or off.on, pumppoff and pumpon setting [This approach was investigated by at least three trials comparing three consecutive CItd measurements with pump setting ,22,23. A setting . Only onComparison of CItd to CIpc during and after stop of blood-flow.Assuming that CIpc is not confounded by RRT, but CItd might be confounded by RRT, potential differences between CItd and CIpc during ongoing blood-flow must be related to confounding TPTD by RRT blood-flow. This approach was used by Dufour et al. and did not show any confounding of CItd by a blood-flow up to 350ml/min .Comparison of CItd to CIpc with ongoing blood-flow.Since in clinical routine a stop of the RRT-pump for haemodynamic measurements might be cumbersome, we compared TPTD-derived CItd to CIpc with ongoing pump. Similar to the previous studies our data confirm that CItd can be accurately measured during RRT.Stability of EVLWI and GEDVI after iso-volaemic “acute” connection to RRT in our study suggests that also these parameters are not substantially confounded by ongoing RRT.effects of iso-volaemic “acute” connection to RRT have not been extensively investigated. In patients with CRF and CRRT a fluid-overload at the onset of RRT is generally assumed. Therefore, patients undergoing chronic RRT are usually connected in a “chronic” modality without pre-filling of the tubing. By contrast, in acute RRT, in particular in critical care patients with high risk of haemodynamic instability, connection to the RRT is established in an “acute” modality, with the tubing being pre-filling. To the best of our knowledge, there are no data using advanced haemodynamic monitoring to investigate the impact of acute modality of connection to RRT in ICU-patients. Our data suggest that connection to RRT in an acute modality does not impair haemodynamic parameters derived from standard monitoring, TPTD or PCA.In general, the While connection to RRT in ARF is aimed at avoidance of circulatory impairment, disconnection usually is performed in a re-transfusion technique to avoid a loss of blood contained within the tubing. In our unit re-transfusion is performed with a blood-flow of 60–100 mL/min resulting in a re-transfusion time of 1.5 to 4 minutes depending also on the volume of the dialyzer used. Several recent papers suggest the usefulness of “mini volume challenges” using fluid boluses of 100 mL infused over one minute ,29. Whil2 and suggests that haemodynamic stability of patients “normal”CI might be overestimated, in particular in a setting with a large proportion of septic patients requiring noradrenalin to keep MAP above 65 mmHg. This might also explain the significant predictive role of CPI, which combines CI and MAP in one parameter. This parameter–whether indexed as CPI or unindexed and termed Cardiac Power Output CPO—has already been demonstrated as the strongest haemodynamic correlate of mortality in cardiogenic shock more than a decade ago [2 might be 0.25 W/m2 or 0.34 W/m2 which is a difference of 36%. With regard to the combined approach of TPTD and PCA in our study, also the association of changes over time in PCA-determined CPI to failure in fluid removal is of interest. Nevertheless, with a ROC-AUC of 0.712, a sensitivity of 77% and a specificity of 55% the predictive capabilities of CPI at baseline have to be classified as moderate.Despite these promising findings, PRRVC has to be considered as an ex-post analysis of fluid removal. Therefore, we chose the a priori prediction of fluid removability as the primary endpoint. In this study the ultrafiltration goal defined immediately before RRT was not associated to the primary endpoint. This indicates that not the amount of ultrafiltration per se, but the individual patient´s condition was decisive for the primary endpoint. Interestingly, two parameters derived from TPTD  were the only significant predictors of feasibility of the pre-defined ultrafiltration goal. The impact of lower values of CItd is remarkable regarding a mean CItd of 4.41±1.41 L/min/mcade ago . Despitecade ago ,31, CPI Overall, this study confirms feasibility and usefulness of TPTD during RRT. Using a different setting compared to previous studies, our data demonstrate that TPTD is accurate despite ongoing RRT. Furthermore, based on advanced haemodynamic monitoring this study shows that “acute iso-volaemic connection” avoids haemodynamic impairment during connection of the patient to the RRT. Additionally, an increase in preload, contractility and markers of general cardiac performance by hypervolaemic disconnection demonstrate a potential for a “post-RRT re-transfusion volume-challenge” (PRRVC) to guide ultrafiltration goals for subsequent RRTs.Baseline CPI and its changes during RRT are promising parameters to guide fluid removal during RRT.Nevertheless, our study has several limitations: It was a single center study with a limited number of patients and repeated measurements in single patients. Our study was performed using a low blood flow of 150 mL/min, and with CVC and dialysis catheters strictly in different positions (one in vena cava superior and the other one in vena cava inferior). Therefore, it is not known, if our findings can be transferred to other RRT settings and catheter positions. In particular with regard to high-flow extracorporeal organ support such as extracorporeal membrane oxygenation (ECMO) our data has to be interpreted with caution.Furthermore, we could not include dynamic indices of fluid responsiveness such as stroke volume variation (SVV) and pulse pressure variation (PPV) in our analysis. Several studies in the peri-operative setting demonstrated superior prediction of fluid responsiveness by SVV and PPV compared to filling pressures and end-diastolic volumes, However, the applicability of these parameters is low in the ICU-setting due to the frequent lack of sinus rhythm and controlled mechanical ventilation, which are mandatory criteria for the use of SVV and PPV –35. Thesbefore inclusion in the study. Previous TPTD measurements might have been useful to balance preload, MAP and CI. Therefore, ultrafiltration goals and achieved rates of about 1.5 L were moderate and not aimed at massive de-resuscitation.Overall, haemodynamic changes induced by SLED with an ultrafiltration of about 1.5 L were—at best—moderate and did not result in substantial decreases in mean preload parameters, MAP and CI over time. However, it has to be kept in mind that these patients were on haemodynamic monitoring TPTD is feasible during SLED. “Acute” connection does not substantially impair haemodynamics. Disconnection with re-transfusion increases preload, CI and CPI. The extent of these changes might be used as a “post-RRT volume change” to guide fluid removal during subsequent RRTs.CPI is the most useful marker to guide fluid removal by SLED.


Androgen receptor (AR) is a ligand-activated transcription factor that is the main target for treatment of non-organ-confined prostate cancer (CaP). Failure of life-prolonging AR-targeting androgen deprivation therapy is due to flexibility in steroidogenic pathways that control intracrine androgen levels and variability in the AR transcriptional output. Androgen biosynthesis enzymes, androgen transporters and AR-associated coregulators are attractive novel CaP treatment targets. These proteins, however, are characterized by multiple transcript variants and isoforms, are subject to genomic alterations, and are differentially expressed among CaPs. Determining their therapeutic potential requires evaluation of extensive, diverse datasets that are dispersed over multiple databases, websites and literature reports. Mining and integrating these datasets are cumbersome, time-consuming tasks and provide only snapshots of relevant information. To overcome this impediment to effective, efficient study of AR and potential drug targets, we developed the Regulators of Androgen Action Resource (RAAR), a non-redundant, curated and user-friendly searchable web interface. RAAR centralizes information on gene function, clinical relevance, and resources for 55 genes that encode proteins involved in biosynthesis, metabolism and transport of androgens and for 274 AR-associated coregulator genes. Data in RAAR are organized in two levels: (i) Information pertaining to production of androgens is contained in a ‘pre-receptor level’ database, and coregulator gene information is provided in a ‘post-receptor level’ database, and (ii) an ‘other resources’ database contains links to additional databases that are complementary to and useful to pursue further the information provided in RAAR. For each of its 329 entries, RAAR provides access to more than 20 well-curated publicly available databases, and thus, access to thousands of data points. Hyperlinks provide direct access to gene-specific entries in the respective database(s). RAAR is a novel, freely available resource that provides fast, reliable and easy access to integrated information that is needed to develop alternative CaP therapies.Database URL: http://www.lerner.ccf.org/cancerbio/heemers/RAAR/search/ The androgen receptor (AR) is a member of the ligand-activated family of nuclear receptors. Upon activation by androgens, AR binds to androgen response elements (AREs) where it assembles the transcriptional complexes to control the expression of target genes , 2. The At the prereceptor level, efforts focus on targeting the key cellular events that control androgen biosynthesis in CaP cells , 12, 13.At the post-receptor level, the characterization of the AR cistrome and AR-dependent transcriptome has facilitated recognition of gene-specific contribution of coregulators and secondary transcription factors to AR-dependent transcription , 25–27. Renewed insights into the mechanisms by which CaP recurs despite ADT are uncovering novel approaches for therapeutic intervention at the AR pre-receptor and post-receptor levels , 9. The To overcome this barrier to the study of AR in CaP, we have developed a novel tool, the Regulators of Androgen Action Resource (RAAR) database. The goal of RAAR is to accrue and provide information in a user-friendly and freely available manner to facilitate the development of novel therapies to inhibit AR action in CaP.RAAR gives access to information at two levels. First, it provides an up-to-date database of genes involved in pre-receptor and post-receptor regulation of AR signaling axis activity. Second, RAAR integrates access to well-established and well-curated databases that provide user-friendly and ready-to-use comprehensive information on gene function, clinical relevance, and technical details for research resources for each RAAR entry. Currently, RAAR contains entries for 329 genes involved in AR action in CaP and links to > 30 databases.RAAR gene entries are classified according to the function for which the gene is known, either pre-receptor or post-receptor. To date, the scenario in which a gene can be entered under both the pre-receptor and post-receptor sections applies only to the AKR1C3 gene. A wealth of reports substantiate AKR1C3’s involvement in multiple intracrine androgen biosynthesis pathways , but onlde novo synthesis of DHT, which starts from acetate  compared to our previously reported inventory  a gene-specific entry (see below) could not be established in the pre- or post-receptor sections, yet the resource itself is relevant for study of the gene of interest listed in RAAR or may give more detail on entry-specific insights derived from the pre-receptor or post-receptor section. Information maintained in other databases is complementary but not identical to what is provided already in RAAR . Links to databases providing specific tools that allow one to compare and analyze genes and gene signatures and regulatory DNA sequences in more detail  are included also. A link to Nucleic Acid Research’s Molecular Biology Databases, several of which may be helpful in enhancing the value of RAAR-derived insights, and links to homepages of the databases listed in http://www.lerner.ccf.org/cancerbio/heemers/RAAR/search/) that is accessible also via a link on the senior author’s (H.V. Heemers) laboratory web site (http://www.lerner.ccf.org/cancerbio/heemers/). Gene lists of critical regulators of intra-CaP androgen biosynthesis and AR-associated coregulators were converted into CSV format files, which were used as input for a MySQL database. Database access is provided by a user interface in PHP and AJAX and a web interface that runs on a Linux (2.6.18)—Apache (2.0) MySQL (5.0.77)—PHP (5.3.6) platform. The MyISAM database storage engine manages non-transactional tables, provides high-speed storage and retrieval and supports full-text searching. The search interface allows searching the pre-receptor and the post-receptor databases by official gene symbol; the other resources database is searched by database name .For some RAAR entries, no content is available in the selected external databases. In those cases, RAAR includes no hyperlink for that entry, which is reflected in a ‘no information’ statement on the results page. Hyperlinks for these RAAR entries will be added as the corresponding database information becomes available.RAAR content is curated manually by Heemers lab members every 3 months. The content of the external databases to which hyperlinked access is provided is monitored frequently. In the event that major updates or additions to external databases occur prior to scheduled RAAR maintenance, RAAR will be updated at an accelerated schedule.RAAR@ccf.org.RAAR strives to be a resource that provides fast, reliable and easy access to integrated information on key regulators of AR action. Recommendations for updates, improvements and additions are welcome. Readers and users are encouraged to forward their suggestions to Improving ADT so that treatment is biomarker-driven and individualized requires complementary expertise in molecular biology, genomics, clinical urology and bioinformatics. RAAR provides not only an up-to-date overview of key regulators of AR activation and transcriptional activity, but brings together in one place an array of diverse resources to facilitate access to multidisciplinary information via simple, user-friendly searches. An advantage of the compendium side of RAAR is it the ease with which the user can study simultaneously the co-operativity, or redundancy, between genes in RAAR. This is an important consideration in view of compensatory mechanisms that govern the activity of both steroidogenic enzymes and coregulators. At the molecular level, RAAR allows the user to, for instance, identify and compare easily the catalytic sites that are present in different steroidogenic enzymes. From a translational perspective, the RAAR user can determine co-expression patterns of regulators and downstream effectors of AR action, correlate differential expression with CaP outcome and survival, and verify the CaP-specificity of these events. For example, merging molecular and clinical aspects, RAAR allows one to evaluate the impact of a genomic alteration that is associated with clinical CaP progression on its protein function, protein interactome and technical resources that are available to pursue the observation further at the bench.The topic of RAAR, namely the genomic action of a nuclear receptor, is similar to, but does not overlap with, other publicly available resources, such as those included in Nucleic Acids Research’s Molecular Biology Database Collection. The AR mutation database , which pRAAR was established by CaP researchers seeking to understand better and target more efficiently AR action in this endocrine cancer. The wealth of diverse data on AR action that is included also makes RAAR a valuable resource for investigators who study other human conditions in which AR is a viable but poorly understood target for therapy. For instance, AR is starting to be recognized as attractive target in triple-negative (ER-/PR-/Her2-) breast cancer  and may


AR CAG and GGN repeat length polymorphisms and Epithelial Ovarian Cancer (EOC) risk in a two-stage, case-control study among Chinese women. The repeat length was analyzed as a categorical variable for CAG_A and GGN_A , CAG-S and GGN_S , CAG-L and GGN_L , respectively. The median value of the repeat length among the controls was used as the cutoff point. Women with longer AR CAG repeats had a decreased risk of developing EOC. The results was replicated in an independent samples. Compared to those with shorter (<22) CAG_A repeat length, women with longer (≥22) CAG_A repeats length had a 31% decreased EOC risk . For CAG_S and CAG_L, the results remain consistent. However, we didn't detected any significant associations for GGN_A, GGN_S, and GGN_L. This should be the first study to examine the association between AR repeat length polymorphisms and ovarian cancer risk in a relatively large group of Asian women.Ovarian cancer is the main cause of death among women with gynecological malignancies. Androgen and its receptors play an important role in ovarian cancer pathogenesis. Here, We aim to evaluate the relationship between  According to American Cancer Society's report, ovarian cancer ranks 5th overall for cancer death in women, accounting for 5% of all cancer deaths in women . AlthougAR is a nuclear transcription factor that mediates the actions of testosterone and dihydrotestosterone [AR protein in cancer specimens has been detected [AR gene [AR gene. The CAG trinucleotide repeat codes for a polyglutamine tract which normally ranges from 6 to 39 repeats [AR decreases with increasing number of glutamines encoded by the CAG repeat tract [AR protein levels were inversely affected by the GGN repeat length due to the reason of the GGN repeat forming a hairpin structure in AR mRNA [Epidemiologic and biological data suggest a role for androgens and androgen receptor (AR) in ovarian cancer development , 7. The osterone . Since tdetected . In part[AR gene , 10. Tog repeats . Moleculat tract . The bio AR mRNA .AR repeat polymorphisms and ovarian cancer [AR repeat polymorphisms in the carcinogenic process, we carried out a two-stage case-control study in a Chinese population to investigate the possible relationship between these two AR repeat polymorphisms and the risk of Epithelial ovarian cancer (EOC) in the Chinese population.There are several published studies that addressed the association between n cancer , 14–17. P = 0.028; Stage1: P = 0.0009), which indicated that usage of hormone replacement therapy might be a potential risk factor for EOC risk. The most histological subtype was serous (71% in Stage 1 and 73% in Stage 2). Figures Table AR CAG and GGN repeat length and EOC risk in stage 1. For CAG polymorphism, women in the category of longer (≥22) CAG_A repeats had a significant 34% decreased EOC risk , when compared to those with the shorter (<22) CAG_A repeat length. The results were consistent and also statistically significant for the CAG_L and CAG_S alleles. For GGN polymorphism, we didn't detected any significant associations for GGN_A, GGN_S, and GGN_L. Then association of AR CAG repeat length with EOC risk was validated in an independent stage 2 samples. As shown in Table P = 5.06 × 10−11), when compared to those with the shorter (<22).Table AR CAG and GGN repeat polymorphisms and EOC risk in Chinese women. We identified that women with longer AR CAG repeats had a decreased risk of developing EOC. To our knowledge, this is the first study to examine the association between AR repeat length polymorphisms and ovarian cancer risk in a relatively large group of Asian women.In this two-stage, case-control study, we examined the relationship between AR is codified by the AR gene which is located on the X chromosome (q11.2–q12). Two length polymorphisms were located at the exon 1: a 9–39 CAG repeat  and a 14–27 GGN repeat . Together, these polymorphisms make 90% of women heterocygotic for the AR gene [AR repeat polymorphisms was suggested by inversed associations between prostate cancer risk and CAG repeat length within the AR transactivation domain in 1995 [AR repeat polymorphism and ovarian cancer risk previously, although the results were inconsistent [AR CAG repeat length with risk of ovarian cancer in Jewish Israeli women who are BRCA1/2 mutation carriers, and null results were concluded due to the small sample size. Since, several studies published recently found shorter CAG repeat length could increase risk of ovarian cancer [The  AR gene . First,  in 1995 . Then, se q11.2–q. Two lennsistent . Spurdlensistent  first she q11.2–q. Two lenn cancer , 25, whin cancer , 23. Onen cancer , who repStrength of this study includes large sample size and the two-stage study design, which is more efficient on discovering potential risk factors. Limitations of our study, including potential biases and population generalizability, should also be taken attentions. Because the study population is primarily Chinese women and our analyses are restricted to Chinese, our results are not generalizable to other ethnicities. However, the homogeneity of our population is an advantage. Due to our case-control design, we must also consider the possibility of selection bias.Our findings suggest that shorter CAG repeat length, modulates the actions of androgens in ovarian neoplasia and tumor growth. These data add to the growing body of evidence linking androgenicity to the pathogenesis and tumor biology of ovarian cancer. Functional studies characterizing hormone activity and response pathways may reveal specific mechanisms in which androgens function in ovarian cancer biology.There were 1,925 EOC cases and 1,900 cancer-free controls included in Stage 1, and Stage 2 included an additional EOC 870 cases and 900 controls. All subjects were genetically unrelated ethnic Han Chinese. Controls were frequency-matched to the cases by age (the range of five years), and residential area. All cases were newly diagnosed according to International Classification of Diseases for Oncology (ICD-O) codes. All the controls were genetically unrelated with cases. Subjects with histories of cancer were excluded from the control group. We also obtained clinical information for all the cases. After informed consent was obtained, a questionnaire about lifestyle factors was completed by all the subjects through face-to-face interviews. After the interview, each subject provided 3–5 mL of venous blood. Approval for this study was obtained from the institutional review boards. Written informed consent was obtained from all participants or from the patients’ representatives.AR CAG repeat polymorphism, while using the primers  for AR GGN repeat polymorphism, using the conditions as follows: with an initial denaturation step at 96°C followed by 35 cycles of 1 min at 96°C, 45 s at 61°C, and 2 min at 72°C, and a final extension step for 5 min at 72°C. Fragments were then run on denaturing polyacrylamide gels on the Applied Biosystems Prism 3700XL and analyzed by Applied Biosystems Prism Genescan automated fluorescence detection .DNA from blood of patients and control specimens was extracted from white blood cell fractions using the Qiagen Blood Kit . Then DNA was PCR-amplified using fluorescently labeled primers  for the t-test was used to test the differences for the AR repeat lengths between case patients and control subjects. Otherwise, the Mann-Whitney U test was used. Odds ratios (ORs) and their 95% CI were calculated from unconditional logistic regression analyses. The repeat length was analyzed as a categorical variable for CAG_A and GGN_A , CAG-S and GGN_S , CAG-L and GGN_L , respectively. The median value of the repeat length among the controls was used as the cutoff point. If some females have two alleles for the same repeat number, average allele, shorter allele, and longer allele share the same number without identifying which is longer. All statistical tests were 2-sided and a P value significance threshold of 0.05 was set. All analyses were conducted using SPSS 19.0.We used the Chi-square test to examine the differences in the frequency distributions of categorical variables between groups. The Shapiro-Wilk test was used to verify the normality of distribution, and Levene's test for equality of variances. When Both of these assumptions were met,