The prevailing paradigm for the analysis of biological data involves comparing groups of replicates from different conditions (e.g. control and treatment) to statistically infer features that discriminate them . However, many situations in modern genomics such as single-cell omics experiments do not fit well into this paradigm because they lack true replicates. In such instances, spectral techniques could be used to rank features according to their degree of consistency with an underlying metric structure without the need to cluster samples. Here, we extend spectral methods for feature selection to abstract simplicial complexes and present a general framework for clustering-independent analysis. Combinatorial Laplacian scores take into account the topology spanned by the data and reduce to the ordinary Laplacian score when restricted to graphs. We demonstrate the utility of this framework with several applications to the analysis of gene expression and multi-modal genomic data. Specifically, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate differentially expressed genes according to the topology of the expression space . We also apply this formalism to identify genes with spatial patterns of expression using fluorescence in-situ hybridization data and to establish associations between genetic alterations and global expression patterns in large cross-sectional studies. Our results provide a unifying perspective on topological data analysis and manifold learning approaches to the analysis of large-scale biological datasets. Manifold learning methods have emerged as a way of analyzing the large high-dimensional data sets that are currently generated in many areas of science. They assume the data has been sampled from an unknown manifold which is approximated with a graph and utilize spectral graph techniques to perform unsupervised feature selection and dimensionality reduction. However, graphs provide only partial approximations to manifolds, precluding the application to features with a complex combinatorial structure. Relatedly, these methods cannot take into account the topology of the manifold. In this work, we extend spectral methods for feature selection to topological spaces built from data and present a general framework for feature selection. We present specific applications of this framework to clustering-independent analysis of gene expression and multi-modal genomic data. In particular, using these methods, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate the results according to topological features of the expression space. In addition, we identify genes with spatial patterns of expression using spatially-resolved transcriptomic data and establish associations between genetic alterations and global expression patterns in large cross-sectional cancer studies. PLOS Computational Biology Methods paper.This is a th century, numerous non-parametric tests were developed to that end, including the Kolmogorov-Smirnov, Wilcoxon, and Mann-Whitney tests, which have become ubiquitous across all areas of science. In these problems, classes are inherent to the formulation of the problem or emerge from an underlying metric or semi-metric structure on the data which permits grouping samples into clusters and subsequently identifying differential features.The standard paradigm for the design of biological experiments involves grouping samples and experiments into replicates from different conditions  and using the observed variability within replicates to assess the significance of the features that differ across conditions. The identification of features that discriminate samples from discrete and identifiable classes is a classical problem in statistics and data analysis. During the 20However, in modern genomics, there are many situations where replicates are not available. For example, true replicates are generally not possible in single-cell omics experiments as each cell is unique and typically can only be measured once. In those situations, the majority of the existing algorithms follow a two-step approach inspired by the above paradigm. First, samples are clustered based on some similarity measure; then, an analytic algorithm  is applied to identify features that discriminate clusters, assuming the observed variability within each cluster is technical. This approach, however, has some important limitations. Very often samples cannot be naturally grouped into clusters, such as when they are drawn from a continuous process. Moreover, much of the variability within clusters is often caused by underlying dynamic and continuous biological processes (e.g. the cellular context in the case of single-cell omics data). In those situations, common tests of significance cannot be applied and feature selection becomes cumbersome.manifold learning methods, such as the Laplacian score [A standard approach to feature selection when samples cannot be arranged into classes is to use variance as a proxy . This apan score  and diffan score , have bean score , 5 and nan score –8 algorian score , 10. HowIn this paper, we introduce a general framework for unsupervised feature selection which extends current graph-based manifold learning methods  and we dWe demonstrate the utility of this framework in the analysis of genomic data and present several applications where spectral simplicial methods lead to qualitatively different results compared to prevailing clustering-based methods. In particular, we use our framework to identify and disaggregate differentially expressed genes according to topological features of the expression space in situations where samples cannot be grouped into distinct clusters. We also show its utility for the analysis of multi-modal genomic data in two different applications: the identification of genes with spatial patterns of expression and the identification of associations between genetic alterations and global expression patterns in large cross-sectional studies. All of these applications represent novel uses of spectral methods in genomics and illustrate the potential of these methods over traditional clustering-based approaches in situations where samples cannot be naturally arranged in groups.Genomic data sets can often be described as point clouds, which are collections of points with a notion of pairwise distance or dissimilarity, such that each point represents a sample. Common examples of pairwise dissimilarities used in genomics are correlation distance among gene expression profiles or genetic distances among genomic sequences. In this paper, we address the problem of unsupervised feature selection in point clouds. We define features of a point cloud as functions that assign a value to each sample. The expression level of a gene, the presence or absence of a mutation, or the methylation level of a gene promoter are some examples of point cloud features.Rr ranks features according to their consistency with the structure of the nearest neighbor graph. Specifically, features with small values for Rr take high values in highly connected nodes of the graph. Although this approach to unsupervised feature selection is rarely used in the analysis of biological data, it has become widespread in other areas of data analysis, as it offers a substantial statistical power compared to ranking features according to their variance [In 2006, He, Cai, and Niyogi proposed an algorithm for unsupervised feature selection called Laplacian score [To be able to capture more complex relations of the data, we generalized the Laplacian score to simplicial complex representations of the data . Like grA simple approach to constructing a simplicial complex from a point cloud is building a Čech complex . This isq-dimensional topology of a simplicial complex built from the data  tε (or the corresponding parameters of UMAP and Mapper) such that the number of rejected null hypotheses at a fixed false discovery rate (FDR) is maximized. The process is illustrated in The statistical significance of the combinatorial Laplacian score can be estimated by randomization. For each point cloud feature, it is possible to build a null-distribution for ocedures , 18, it The combinatorial Laplacian score can also be naturally extended to pairs of features, in analogy to the notion of covariance for pairs of random variables . The bivWe implemented the computation of the 0- and 1-dimensional combinatorial Laplacian score and the 0-dimensional bivariate score in an open-source R package (Methods). In this implementation, the computation of the 0-dimensional combinatorial Laplacian score can be performed in a standard desktop computer for point clouds involving thousands of samples . The comA standard task in genomics is the identification of differentially expressed genes among discrete groups of samples  whose transcriptome has been profiled using expression microarrays or RNA-sequencing. However, in many situations samples cannot be naturally arranged or clustered into discrete groups based on their transcriptome. Cells (and the tissues they form) often respond to stimuli from their cellular environment in a continuous way. For example, immune cells acquire immunosuppressive or pro-inflammatory phenotypes in different contexts and there is a continuum of cellular phenotypes interpolating among these states. Furthermore, the transcriptome of a cell is the result of many concurrent molecular pathways taking place in the cell. In this section, we utilize the combinatorial Laplacian score to identify differentially expressed genes without predefining groups of samples or cells, as opposed to conventional approaches to differential expression analysis where at least two groups of samples need to be specified. We focus on the particular case of single-cell RNA-seq data.We assessed the power of the 0-dimensional combinatorial Laplacian score to discriminate differentially expressed genes as compared to conventional methods. To that end, we followed the same approach as in a recent comparative study of single-cell differential expression analysis . We consFGFBP2, which is expressed exclusively and ubiquitously by natural killer cells. However, this cluster structure does not account for the expression pattern of many of the top differentially expressed genes identified by the combinatorial Laplacian score. For instance, among the cytotoxic CD8+ T-cells, we observe distinct subpopulations of cells expressing combinations of Hepatitis A Virus Cellular Receptor 2 (HAVCR2), Radical S-Adenosyl Methionine Domain Containing 2 (RSAD2), and Granzyme K (GZMK). The combinatorial Laplacian score thus reveals more complex and richer patterns of cellular heterogeneity than clustering-based methods. We expect this approach to be of great utility in studying the transcriptional programs underlying dynamic cellular systems such as those that occur during development, inflammation, or tissue repair [We next applied the 0-dimensional combinatorial Laplacian score in a case where conventional methods are of limited utility. Specifically, we considered single-cell RNA-seq data of 24,911 T-cells infiltrating lung tumors and adjacent normal tissue . T-cellsThe application to differential expression analysis described in the previous section only makes use of the graph structure of the simplicial complex. Higher-dimensional combinatorial Laplacian scores can be used to disaggregate differentially expressed genes according to their degree of localization along topological features of the data. For example, in dynamic cellular systems involving convergent lineages, strong cell-cycle effects, or complex differentiation paths, cells may span loops in the expression space. In those situations, the 1-dimensional combinatorial Laplacian score permits identifying genes that are differentially expressed along the loops.in vitro differentiation of mouse embryonic stem cells into motor neurons using two different protocols [p-value < 10−6). These included genes, such as Lhx3 and Plk3, which are known to be upregulated in one of the two differentiation protocols . On the other hand, genes with significantly upregulated expression in other regions of the expression space only had a low 0-dimensional combinatorial Laplacian score (−6) but not a low 1-dimensional score  into motor neurons (MNs) [q-value = 2×10−7) as the most enriched biological process among genes in the lowest quartile of both the 1-dimensional and the 0-dimensional Laplacian scores. To the contrary, the most enriched ontologies among genes in the lowest quartile of the 0-dimensional Laplacian score but the top quartile of the 1-dimensional Laplacian score were neuron differentiation , axonogenesis , and other non-cyclic biological processes.Loops in the expression space of cells can also be produced by other effects unrelated to the convergence of differentiation trajectories, such as cell cycle effects. In a previous study, the Mapper algorithm was used to construct a topological representation of the expression space of the ns (MNs) . In thatTaken together, these three examples show that, when the expression space has a complex topology, different combinatorial Laplacian scores allow the identification and categorization of differentially expressed genes according to the topology of the expression space without pre-defining cellular populations or clustering cells.Spectral methods rank features according to their degree of consistency with a given metric structure, as we have described. However, the data type of the features may differ from that of the metric structure. Spectral methods can therefore be adapted to perform multi-modal analyses, where features of a given type are ranked according to their degree of consistency with a metric structure of a different type. To show the utility of this type of analysis, we considered two specific applications to multi-modal genomic data.in-situ hybridization (FISH) data of the murine somatosensory cortex [Using spatially-resolved transcriptomics, the expression level of genes can be measured in their spatial context, often with single-cell resolution. We considered single-molecule fluorescence y cortex  and utilq-value ≤ 0.01; n = 1,000 permutations), we observed numerous known cancer-associated genes in adult brain glioma. The significance of this observation was confirmed by a gene-set enrichment analysis using the recurrently-mutated genes reported by MutSig2CV in the same cohort . These results demonstrate the potential of the Laplacian score to establish associations among multiple types of genomic data when samples cannot be arranged into discrete classes.Another instance where multi-modal data analysis is of prime importance is quantitative trait association studies. These studies seek to establish associations between quantitative phenotypes (e.g. gene expression levels) and genotypes. To demonstrate the utility of the combinatorial Laplacian score to establish associations between genetic variants and complex high-dimensional phenotypic spaces, we analyzed somatic mutation data, obtained by whole-exome sequencing, and mRNA expression data of a cohort of 667 low-grade glioma and glioblastoma tumors . We usedModern data often comes in the form of large point clouds. A common approach to analyzing these point clouds involves clustering the data based on the underlying metric structure and using some statistical test to identify features that differ across clusters. However, there are multiple situations where data cannot be naturally structured into clusters. Even in the cases where clustering is possible, there are always basic trade-offs inherent to the clustering problem . MoreoveThe framework we have introduced has natural applications in the analysis of genomic data when the data cannot be comprehensively and stably described by clusters. This situation occurs frequently in datasets consisting of many samples, such as those derived from high-throughput single-cell omics experiments (thousands of cells) or large cross-sectional studies (thousands of specimens). The resolution of these datasets is such that they often capture continuous patterns of variation that cannot be faithfully represented by clusters. Some of these studies involve multiple concurrent data modalities  and we have shown that spectral simplicial methods can be also naturally applied in those situations. As the scale and complexity of genomic datasets continue to increase, we expect these methods will become a particularly useful tool for the analysis of genomic data.RayleighSelection (https://github.com/CamaraLab/RayleighSelection). To improve performance, we used parallelization and embedded C++ code for the computationally demanding parts. Additionally, we included routines for the generation and visualization of simplicial complexes.The 0- and 1- dimensional combinatorial Laplacian scores are implemented in the open-source R package ε = 0.7, as determined by maximization of the number of rejected null hypotheses at a fixed FDR of 0.05 (Benjamini-Hochberg procedure). The p-values were estimated by randomization with 5,000 permutations.We performed feature selection on the MNIST dataset using the 0- and 1-dimensional Laplacian scores. Each sample consists of a 28x28 grey-scale image of a handwritten digit. In our analysis, each feature represents the intensity of a pixel across all images. A Vietoris-Rips complex was built from the Pearson’s correlation distance between 100 handwriting samples using the top 25% pixels with highest variance. The Vietoris-Rips parameter was set to ε was in all cases determined by maximizing the number of rejected null hypothesis at FDR = 0.05. The p-value of the 0-dimensional Laplacian score was calculated for each gene by randomizing the data with 5,000 permutations. We then computed the AUC by comparing with the actual simulated differentially expressed genes. We ran the software edgeR [We used the 0-dimensional Laplacian score to identify differentially expressed genes in three simulated datasets generated by Soneson and Robinson  based onre edgeR  using thε = 0.7, FDR = 0.1). To reduce the computational time, we only used 1,000 randomly sampled cells and the top 2,800 genes with highest variance to build the simplicial complex. Expression values were transformed to log(1+TPM). We computed the 0-dimensional Laplacian score for the 7,242 genes that were expressed in more than 1% and less than 25% of cells.We performed differential expression analysis of single-cell RNA-seq data from 24,911 T-cells infiltrating lung tumors and adjacent normal tissue from Lambrechts et al. . We usedhttps://github.com/AllonKleinLab/SPRING) to visualize and confirm the cyclic nature of the expression data. The first 50 principal components output by SPRING were then used to calculate a Euclidean distance matrix and a Vietoris-Rips complex for 200 randomly sampled cells, where the value of the parameter was determined by maximizing the number of rejected null hypothesis . An orientation in the complex was determined by ordering the cells according to their graph distance from one of the terminally differentiated cells in the SPRING representation. We computed the 0-dimensional and 1-dimensional Laplacian scores for all the genes with expression detected in more than 5% and less than 50% of the cells. Statistical significance was estimated by randomization with 5,000 permutations. Using edgeR’s GLM fold-change threshold test (log fold change > 1), we performed differential expression analysis between the intermediate state cells, as defined in Briggs et al. [To demonstrate the efficacy of the 1-dimensional Laplacian score in the analysis of genomics data, we considered the combined scRNA-seq data of two differentiation protocols on mouse embryonic stem cells (GEO Database accession number GSE97390) from Briggs et al. . We procs et al. , and allmin.dist = 0.1, n.neighbors = 15. We used the 0-dimensional and 1-dimensional Laplacian scores to rank gene expression on a Vietoris-Rips complex created from this UMAP embedding (ϵ = 2.4) for all genes expressed in more than 2% and less than 50% of cells. Orientation in the simplicial complex was determined by ordering cells based on their Euclidean distance from a chosen cell at the earliest timepoint. We performed differential expression analysis between the two trajectories annotated by Cao et al. [We extracted cells of the epidermis and AER trajectories from the scRNA-seq dataset published by Cao et al.  and applo et al.  using edWe used the combinatorial Laplacian score to reanalyze the Mapper representation of a motor neuron differentiation dataset previously generated in Rizvi et al. . Mapper 2 or greater than 338 μm2 in size or contained fewer than 40 total molecules. We then used Euclidean distance between the spatial coordinates of the remaining cells to create a Vietoris-Rips complex . We computed the 0-dimensional Laplacian score of the expression of each gene normalized by the total number of cell molecules. Using the same cells, we created a Vietoris-Rips complex  to compute the bivariate combinatorial Laplacian score of the binary cell type assignments. Significance of the bivariate score was computed based on 2,000 random permutations of the cell type labels. We visualized the expression of specific genes and cell populations using a Voronoi tessellation centered on the spatial coordinates of the cells.We considered single-molecule FISH data generated by Codeluppi et al.  for a 32ε = 0.22) using Pearson’s correlation distance and the highest-variance genes (σ > 1) in the log-transformed expression data. Binary vectors indicating the presence of at least one non-synonymous somatic mutation in each gene were taken as features, and the 0-dimensional Laplacian score was computed on these features. To verify that the genes with significant Laplacian scores  are cancer-associated, we performed a gene set enrichment analysis  of the genes reported by MutSig2CV [We re-analyzed somatic mutation data and mRNA expression data of a cohort of 667 low-grade glioma and glioblastoma tumors from TCGA . We creautSig2CV  in the lutSig2CV  represenS1 NoteMathematical appendix introducing the definition and properties of the combinatorial Laplacian score on simplicial complexes and its application to feature extraction.(PDF)Click here for additional data file.S1 FigGene-set enrichment analysis for upregulated genes at intermediate (top) or early/late (bottom) states in the example of the in vitro differentiation of mESCs into MNs using standard and direct programming protocols. Genes are ranked according to their 0- (left) and 1-dimensional (right) combinatorial Laplacian score. Genes with 0-dimensional combinatorial Laplacian score are enriched for genes that are differentially expressed at any stage within the differentiation. Genes with low 1-dimensional combinatorial Laplacian score are strongly enriched for genes that are upregulated at intermediate states, where the alternative paths for differentiation occur.(TIF)Click here for additional data file.S2 FigSkiv2l2) is also displayed.The scatter plot represents the 0- and 1- dimensional combinatorial Laplacian scores of 6,691 genes. A UMAP representation color-coded for expression (grey to red) is shown for some of the top differentially expressed genes identified by this method. Genes with low values of (TIF)Click here for additional data file.S3 FigNf1) is also displayed.The 0- and 1-dimensional combinatorial Laplacian scores were run over the scRNA-seq expression data of the differentiation of mESCs into MNs using the SP protocol. The scatter plot represents the 0- and 1-dimensional combinatorial Laplacian scores of 6,938 genes. A Mapper simplicial complex color-coded for expression  is shown for some of the top differentially expressed genes identified by this method. Genes with low values of (TIF)Click here for additional data file.


To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis.A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests.The gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance .The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy.The online version of this article (10.1186/s13014-018-1140-9) contains supplementary material, which is available to authorized users. Lung cancer is the leading cause of cancer-related deaths worldwide . Lung caRadiomics analysis can extract a large number of imaging features quantitatively, which could offer a cost-effective and non-invasive approach for individual medicine –5. SeverMachine-learning (ML) can be resumptively defined as the computational methods utilizing data/experience to obtain precise predictions . The ML In this study, we investigated the effect of 8 ML and 5 feature selection methods on predicting OS for non-small cell lung cancer based on radiomics analysis. The effectiveness of ML and feature selection methods on the prediction of OS were evaluated utilizing the concordance index (CI) , 13–16.The data used in this study was obtained from the ‘NSCLC-Radiomics’ collection , 17, 18 The flow chart of the prediction process , 21 for For each training fold, the training algorithm required both the training inputs (for prediction) and the prediction targets  data. The training inputs referred to the selected radiomics features, while the prediction targets referred to the OS of the patients. The radiomics features were first extracted from the images and then selected (dimension reduction) using the filter based feature selection methods to reduce the risk of overfitting. Finally, the selected features would be used to optimize and train all the ML models. In this study, the Bayesian optimization method was applied to determine the optimal parameters .For each validation fold, the corresponding selected radiomics features were first extracted from the images and then transferred into the trained model. Finally, the prediction OS would be used to evaluate the goodness of each model.Prior to extracting the radiomics features, we fixed the bin number 32 bins) of all the pre-treatment CT scans to discretize the image intensities. It should be noted that the original voxels for the images were used in this study. Then, the radiomics features were automatically extracted from the GTV region of the CT images by our in-house developed radiomics image analysis software and the Wavelet toolbox based on the Matlab R2017a . Total 43 unique quantitative features in 4 categories  Intensity features: to describe the shape characteristics of the CT volume’s gray-level intensity histogram, i.e., a probability density function (PDF) of gray-level distribution.2) Fine texture features: to describe the high-resolution heterogeneity in the ROI. These features were derived from the ROI’s Gray-Level Co-Occurrence Matrix (GLCOM), a joint PDF that measures the frequency of co-occurring adjacent voxel pairs having the same grayscale intensity at a given direction .3) Coarse texture features: to describe the low-resolution heterogeneity in the ROI. These features were calculated from the ROI’s Gray-Level Run Length Matrix (GLRLM), a joint PDF that measures the size of a set of consecutive voxels with the same grayscale intensity at a given direction .4) Morphological features: to describe the morphological characteristics of the ROI .Here, the first category and the following two (second and third) categories required the intensity histogram and textural image processing steps, respectively. Both the above two image processing steps and the 43 radiomics features used in this study matched benchmarks of the Image Biomarker Standardization Initiative (IBSI) .Moreover, these radiomics features were also extracted from different wavelet decompositions of the original CT image by a three levels wavelet transformation , 28. HowPearson’s (PCC) , KendallThe effect of 8 ML methods were investigated in this study, including: Cox proportional hazards model (Cox) , gradienFor each ML method, the parameters were selected from the combination of parameters that produced the best performance using the three-fold CV on each training fold. Similar procedures were implemented in Brungard et al.  and HeunThe range of parameters used in this study was showed in Table CI with confidence interval (CFI) based on bootstrapping technique (the number of bootstrap samples was 2000 in this study) was used to assess the performance of difference ML methods on the merged validation fold . The percentage of CFI was 95% in this study. A nonparametric analytical approach method proposed by Kang L et al.  and the p-value using the z-test between the above two methods. The p-value of CI between these two methods was 0.5, indicating that the difference of prediction performance between these two methods wasn’t significant. The values selected for the hyper-parameters mentioned in Table Figure p-value of the CoxBoost method with the PCC feature selection method was also significant for each validation fold. The heatmap of p-values on each validation fold for all the ML methods was showed in the Additional file Patients on each validation fold were divided into two groups (low- and high- risk group) based on the predicted risk of each radiomics model at the cut-off value. The cut-off value utilized for stratification was the median of each training fold which would be applied to the corresponding validation fold unchanged. Then, the Kaplan-Meier and log-rank tests methods were used to evaluate and compare the survival curves for each validation fold, respectively. Among all the ML methods, the GB-Cox method with the CI feature selection method obtained the best stratified result on the 3 CV folds Fig. . BesidesSeveral previous studies have compared the prediction performance of the ML models based on the radiomics analysis. Parmar C et al.  identifiAs shown in Fig. Some previous studies , 5 have The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy.Additional file 1:P-values of the log-rank test for all the feature selection and ML methods on each validation fold. (PDF 625 kb)Supplementary A: Feature selection methods. Supplementary B: Machine learning methods. Supplementary C: The values selected for the hyper-parameters on each validation fold. Supplementary D:


Staphylococcus and Enterococcus species. The evaluated partly promising activities depend on the indole substituent type. First lead compounds have been evaluated within in vivo studies. They confirmed the in vitro results for the new classes of small-molecule antibacterials.Defeat of the antibiotic resistance of pathogenic bacteria is one great challenge today and for the future. In the last century many classes of effective antibacterials have been developed, so that upcoming resistances could be met with novel drugs of various compound classes. Meanwhile, there is a certain lack of research of the pharmaceutical companies, and thus there are missing developments of novel antibiotics. Gram-positive bacteria are the most important cause of clinical infections. The number of novel antibacterials in clinical trials is strongly restricted. There is an urgent need to find novel antibacterials. We used synthetic chemistry to build completely novel hybrid molecules of substituted indoles and benzothiophene. In a simple one-pot reaction, two novel types of thienocarbazoles were yielded. Both indole substituted compound classes have been evaluated as completely novel antibacterials against the  Staphylococcus aureus on human surfaces like skin developed early resistances thiophenes have been reported to show antimicrobial activities depending on their substituents [b]thiophene compounds started from a 3- or 7- mostly halogen substituted benzo[b]thiophene scaffold which reacted with its 2-substituent to give 2-urea, -semicarbazide, -acyl or -heteroaryl substituted compounds [b]thiophene scaffold and the substituted phenyl residues was found necessary for such activities [b]thiophenes with aryl substituents, but methods to synthesize fused aryl benzo[b]thiophenes are rare [b]thiophenes is completely different from earlier methods that concentrated on the benzo[b]thiophene derivatization.Benzothiophenes and the indoles showed activities towards S. aureus, we first investigated our compound classes as promising growth inhibitors of S. aureus. The determined minimal inhibitory concentration (MIC) values are shown in As the reported benzothiophenes with a 2-urea phenyl or -semicarbazide phenyl substitution showed mainly reduced activities with MIC values of 40 µg/mL [b]thiophenes with a 2-heteroaryl-phenyl substitution showed similar activities carbazole classes, promising candidates have been identified. They showed best activities towards Staphylococcus in case of a 5-chloro, a 6-cyano and both a 5- and 6-hydroxy indole substitution Most promising compounds in case of the various Enterococcus species have been those with a 5-chloro or -bromo as well as the 6-hydroxy indole substitution. Their activities farly exceed those of the used standard vancomycin. For selected compounds the in vitro results could be confirmed in an in vivo galleria infection model. As they are practically new small-molecule antibacterials, we expect our contribution in the field to be hopeful for further studies. These studies will partly concentrate on the identification of the bacterial target which is addressed by our novel compounds. One suspected target structure for consideration is the bacterial pyruvate kinase that is addressed by structurally-related bis-indolyl derivatives similar to the marine bis-indolyl alkaloid dihydrohamacanthin [Over- and misuse of antibiotics, as well as contamination of the environment with antibiotics and antibiotic waste increase antibacterial resistances. Industrial efforts to develop novel antibiotics have been strictly shortened, so that almost just variations of established antibiotics are available. In case of the increasing resistances against the Gram-positive bacteria istances . Presentistances . So theracanthin .


Alternative splicing (AS) is a major mechanism that greatly enhanced the diversity of proteome. Mounting evidence demonstrated that aberration of AS are important steps for the initiation and progression of human cancers. Here, we comprehensively investigated the association between whole landscape of AS profiles and the survival outcome of renal cell carcinoma (RCC) patients using RNA-seq data from TCGA SpliceSeq. Because of the limited number size of deaths in kidney chromophobe renal cell carcinoma (KICH) and papillary renal cell carcinoma (KIRP) TCGA cohorts, we only conducted survival analysis in kidney clear renal cell carcinoma (KIRC). We further constructed prognostic index (PI) based on prognosis-related AS events and built correlation network for splicing factors and prognosis-related AS events. According to the results, a total of 5351 AS events in 3522 genes were significantly correlated with the overall survival (OS) of kidney clear cell renal cell carcinoma (KIRC) patients. Seven of the PI models exhibited preferable prognosis-predicting capacity for KIRC with PI-ALL reaching the highest area under curve value of 0.875. The splicing regulatory network between splicing factors and prognosis-related AS events depicted a tangled web of relationships between them. One of the splicing factors: KHDRBS3 was validated by immunohistochemistry to be down-regulated in KIRC tissues. In conclusion, the powerful efficiency of risk stratification of PI models indicated the potential of AS signature as promising prognostic markers for KIRC and the splicing regulation network provided possible genetic mechanism of KIRC. Renal cell carcinoma (RCC) is the most prevalent type of adult kidney cancer and is responsible for 14970 new deaths in 2018 worldwide Alternative splicing (AS) is a pivotal process that increases the protein complexity through enabling the generation of multiple splice isoforms from an individual gene The feasibility of AS-based risk signature have been proved in non-small cell lung cancer, ovarian cancer, bladder cancer and gastrointestinal pan-adenocarcinomas According to the including criteria of eligible cases, a total of 62 KICH, 468 KIRC, 122 KIRP type 1 and 53 KIRP type 2 patients were enrolled in this study. The distribution of AS events in the above major subtypes of RCC was summarized in Figure The PPI network in Figure Enrichment of genes of top 1000 prognosis-related AS events in KIRC was recorded detailedly in Table Data of AS events from multivariate Cox regression analysis that were adopted as components of PI models were listed in Table To test the ability of PI models with the strongest predicting efficiency in prognosticating the survival condition of patients in different group of clinical advances, Kaplan-Meier survival analysis was performed to appraise the prognosis-predicting capacity of PI-ALL in stage I-II and stage III-IV groups of KIRC patients, respectively Figure . ExpecteWe obtained information of 66 splicing factors from SpliceAid2 and univariate Cox regression analysis for all the 66 splicing factors indicated 12 prognosis-related splicing factors in KIRC Table . InteresImmunostaining images of KHDRBS3 in ten pairs of KIRC and paracarcinoma tissues were displayed in Figure Currently AS has been proposed as the addition to the growing list of cancer hallmarks Transcriptome-wide analysis of the AS profiling land scape revealed thousands of prognosis-related AS events in KIRC. It is noteworthy that 15 of all the prognosis-related genes possessed four splicing types, involving several genes with critical functions in KIRC such as PLAGL1 and NDRG2. PLAGL1 was originally reported to act as tumor suppressor in several types of human tumors via cell-cycle arresting and pro-apoptotic effect To gain deeper insights into the mechanism of prognosis-related AS events in KIRC, we performed KEGG pathway analysis and PPI network for genes corresponding to top 1000 prognosis-related AS events. KEGG pathway annotation revealed key clues for the function of prognosis-related AS events in KIRC that herpes simplex infection, ribosome and autophagy - other were three most significant pathways enriched by prognosis-related AS event in KIRC. Of the thee terms, autophagy, an evolutionary conserved process that refers to the sequestration and delivery of organelles and macro-molecules to lysosome for degradation Based on the prognosis-related AS events, we constructed eight prediction models grouped by splicing types. Further evaluation from Kaplan-Meier survival analysis and tROC curves reflected the preferable prognosis-predicting ability of seven PI models . Although some prediction models composed of miRNAs or mRNAs have been devised by other researchers It is widely acknowledged that splicing factors were key regulators of AS Despite the encouraging findings in the present study, room for improvements remains for this study. The original intention was to conduct survival analysis of AS events in pan-kidney cancers. However, the small number of deaths restrained us from performing survival analysis for AS events in other subtypes of RCC: KICH, KIRP type 1 and KIRP type 2. There is a lack of testing set of for the verifying the prognosis-predicting ability of PIs in KIRC after the construction of PIs. Moreover, the biological function of AS events in KIRC and the relationships between splicing factors and AS events needed experimental validation. All of these defects pointed out directed the future efforts of our work.In summary, PIs based on prognosis-related AS events were constructed by univariate and multivariate Cox regression analysis of AS profiles in KIRC. Subsequent evaluation results proved that risk-stratification by PI enabled the satisfactory differentiation of survival outcomes in KIRC patients. Additionally, the correlation network between splicing factors and prognosis-related AS events shed lights on the genetic mechanism of KIRC.AS profiles of RCC patients were generated using SpliceSeq, a java program that provides a clear view of the splicing patterns through accurate alignment of reads and exon-inclusion or skipping splice junction  (ß is the regression coefficient). The Kaplan-Meier survival analysis was employed in GraphpadPrism v.7.0 to compare the survival difference of KICH, KIRC and KIRP patients groups divided by the median value of PI. The prognosis-predicting ability of PIs was evaluated by plotting the tROC curves with the aid of survivalROC package in R software v.3.5.1. The predicting efficiency of each PI model was compared at 3000 days for fewer events occurred after 3000 days. Two-tailed P value of less than 0.05 was considered statistically significant. The interactive sets in seven splicing types of AS events were visualized and aggregated by UpSet package of R software v.3.5.1, which exhibited better performance than Venn plot when the number of datasets was more than five Three major types of RCC cases  with OS time over 90 days and PSI information of AS events were eligible for the present study; RCC cases were excluded from survival analysis if the criteria listed above was not met. We performed univariate Cox regression analysis R software v.3.5.1 to judge whether the AS events were prognosis-related or not according to the median of PSI value. Then, top ten prognosis-related AS events in each splicing type from KICH, KIRP type1 and KIRP type2 as well as top 50 prognosis-related AS events in each splicing type from KIRC were subjected to multivariate Cox regression analysis in SPSS v.22.0 for the identification of AS events appropriate for the construction of prediction models. Significant prognosis-related AS events from multivariate Cox regression analysis were included as components of PI models corresponding to each splicing type. Specifically, PI-ALL was built based on multivariate Cox regression analysis results of top ten or 50 prognosis-related AS events from all splicing types. The following formula was applied in calculation of PIs: PI=We uploaded genes of top 1000 prognosis-related AS events in all splicing types from univariate Cox regression analysis to online program: STRING v.10.5.0 for the creation of PPI network. The highest confidence (0.9) was assigned to the PPI network in order to guarantee the credibility of results. Genes in the PPI network with the highest connection degrees were defined as hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for genes submitted to STRING was carried out by ClusterProfiler package of R software v.3.5.1. Terms with p value <0.05 were significant pathways enriched by these genes.www.introni.it/spliceaid.html) Aware of the predominant position of splicing factors in mediating AS, we further investigated the regulatory relationships between splicing factors and AS events for three major types of RCC . Firstly, we downloaded information of humankind splicing factors from SpliceAid 2 (Ten pairs of KIRC and adjacent normal tissues were collected from the First Affiliated Hospital of Guangxi Medical University during the period of September 2017 to February 2019. The experiments were approved by the Ethical Committee of the First Affiliated Hospital of Guangxi Medical University and written informed consent was signed by each participant. The standard procedures of IHC were described in detailed previously


Low-quality social relationships in older adults are strongly associated with feelings of loneliness. Physical activity interventions could reduce loneliness and improve psychological well-being, among other health benefits. The aim of this study is to examine the feasibility of a Physical Activity Intervention for Loneliness (PAIL) in community-dwelling older adults at risk for loneliness.This feasibility study is a two-arm randomised controlled trial (RCT) with a wait-list control group using a mixed-methods research design. The primary aim of the feasibility study is to estimate recruitment, retention and adherence rates; the appropriateness of the intervention design and its practicality; the acceptability of the intervention by participants; and the set of instruments and measures and primary outcome measures to inform a future large-scale randomised trial. After eligibility screening, randomisation will be conducted using computer-based random sequence generation. Baseline and post-intervention assessments for intervention and control groups will include height, weight, body mass index, resting blood pressure, physical activity using accelerometry, loneliness, social support, social networks, anxiety and depression, self-efficacy for exercise, satisfaction with social contacts, and expected outcomes and barriers for exercise using questionnaires. Focus groups will be conducted at the mid-point and post-intervention period using a phenomenological approach to analyse the participants’ experiences of taking part in PAIL.This trial will provide important information regarding the feasibility of PAIL in community-dwelling older adults at risk for loneliness using a mixed-methods approach combining quantitative and qualitative research methods.Clinicaltrials.gov NCT03458793The online version of this article (10.1186/s40814-018-0379-0) contains supplementary material, which is available to authorized users. Maintenance of social connectedness throughout the lifespan is an important aspect of successful ageing . The disPhysical activity (PA) is defined as any bodily movement produced by the contraction of skeletal muscles that results in a substantial increase over resting energy expenditure , 14. It Mechanisms of physical activity interventions’ effectiveness are suggested to relate to loneliness reduction models, stress reduction and increased social support during activities. Related to the first mechanism, the social compensation model  suggestsBased on the analysis of existing randomised controlled trials (RCTs), there are few PA interventions for loneliness reduction conducted with residents in community settings . This isFurther, a number of moderating (affecting the strength of the relationship between PA and loneliness) and mediating factors (driving the influence of PA on loneliness) are not consistently considered in intervention studies, making the outcomes of even well-conducted interventions less credible . ResearcBearing in mind the limitations of the current literature as presented above, understanding the mechanisms of association between loneliness and PA may bring new insights to the designing of novel and effective PA interventions . FurtherRecruitment rate, attendance and retention rates (number of participants completing the study as a proportion of those randomised)The appropriateness and practicality of the designed intervention in the proposed settingsThe acceptability of the intervention by participants and willingness to participateThe appropriateness of the assessment toolsThe appropriateness of the statistical methods of data analysis usedThe power calculation of the likely required sample size for a future large-scale RCT.The acceptability of measures and the most suitable primary outcome measure for a future large-scale RCTThe following specific aims of this feasibility study are to estimate:In addition, to reflect the aims of a future large-scale RCT that this feasibility study seeks to inform, the effect sizes between the intervention and control groups will be examined.PAIL is a tw0-arm RCT of a 12-week intervention compared with a wait-list control (WLC) condition using a mixed-methods research design . Quantitative data will be collected from the intervention and control groups at the baseline and immediate post-intervention periods. Qualitative data aimed to assess the appropriateness, practicality and acceptability of the designed intervention in the proposed settings will be collected at the mid-point and post-intervention period. The trial was approved by the Science, Technology, Engineering and Mathematics (STEM) Research Ethics Committee of the University of Birmingham, UK (ERN_16-1419A). Consent forms  and community centres across the wide geographic area of Birmingham, UK.Community-dwelling older adults aged 60 years and older;Previously sedentary (i.e. as defined by a lack of regular involvement in more than 20 min exercise per week over the past month that increased breathing significantly and was considered moderate) ;At risk of loneliness and having ≥ 6 out of 9 points on the three-item loneliness scale during the phone screening  Additio;Physically mobile as measured using the Short Physical Performance Battery (SPPB)  with a sHealthy or having one or more common chronic diseases but ambulatory, without a cognitive disability as assessed by the Montreal Cognitive Assessment (MOCA)  with a sAble to give written informed consent;English speaking and able to complete paper and pencil questionnaires.The following are the inclusion criteria:< 60 years old;Currently taking part in another physical activity intervention;Socially active or not lonely based on the phone screening tool by Hughes et al. ;Regularly physically active;Moderate to severe cognitive disability with cut-off below 22 for MOCA or clinical diagnosis of dementia or Alzheimer’s disease;Not ambulatory, i.e. not able to walk 4 m;Not literate in English (speaking and reading) as this precludes taking pen and paper tests.The following are the exclusion criteria:The PAIL feasibility study is a 12-week intervention consisting of group walking and health educational/social interaction workshops performed once weekly for a duration of up to 90 min per session. The design and features of the PAIL intervention are based on the features of effective interventions that were obtained from a systematic review and meta-analysis of the existing evidence conducted by Shvedko et al. . Effectimax) method [Group walking sessions will be run once weekly for up to 45 min each in small groups (up to eight to nine people per group) and delivered by a trained walk leader . Prior to the first walking session, participants in the intervention group will be asked to complete the “Par-Q and You form”  and will) method  by calcu) method  and the ) method . Partici) method , 49 and ) method  to enhan) method . Light-t) method  and 2–4 ) method . A warm-) method . A furthGroup walking sessions will be followed by the health education/social interactions workshops delivered in the form of a group presentation weekly for up to 45 min by the researcher (PhD student) on a variety of healthy ageing topics such as eye hygiene, mental health and well-being, preventing falls, social support, nutritional guidelines and physical activity recommendations for older adults  group will start the intervention after their follow-up measures are completed approximately 12 weeks post-randomisation.All measures will be conducted at the host academic institution at baseline and at the immediate post-intervention period Fig.  except fParticipants will provide socio-demographic information including their age, gender, ethnicity, marital status, living arrangements, level of education, any children, employment status and any medical conditions.Cognitive function will be assessed using a Montreal Cognitive Assessment (MOCA) scale designed to test mild cognitive impairment [Physical functioning will be assessed using the Short Physical Performance Battery (SPPB) [Height to the nearest 0.1 cm will be measured using a stadiometer  and recorded in metres. Weight will be assessed using weighing scales  to the nearest 0.1 kg. Body mass index  will be calculated by dividing mass (kg) by height squared (m2). Resting blood pressure  will be measured using a portable semi-automatic OMRON sphygmomanometer . Physical activity will be measured using ActivPAL accelerometers  at baseline and immediate post-intervention period over a continuous 7-day period of awake and sleeping (24 h a day) except when bathing or swimming [pairment . Physicay (SPPB) . Height swimming .Loneliness will be assessed using the 8-item UCLA Loneliness Scale (UCLA-8) [Social support will be assessed using the 20-item Medical Outcomes Study Social Support Survey (MOSSSS) [Social networks will be assessed using the 6-item Lubben’s Social Network Scale (LSNS-6) [Depression and anxiety will be assessed using the 14-item Hospital Anxiety and Depression Scale (HADS) [Self-efficacy for exercise will be measured using the revised 9-item Self-Efficacy for Walking/Exercise Scale (SEE) using a paper-and-pencil format [Satisfaction with level of social contacts will be measured with the question “How satisfied are you with your social contacts?” [Expected outcomes and barriers for exercise will be administered using the Expected Outcomes and Barriers for Habitual Exercise scale [(UCLA-8) . Social (MOSSSS) . Social (LSNS-6) . Depresse (HADS) . Self-efl format . Satisfantacts?” . Expectese scale  adapted se scale . The expse scale .To understand participants’ experiences of taking part in the PAIL feasibility trial, focus groups will be conducted at mid-point (between the weeks 4 and 5) and at the end of the 12-week intervention using semi-structured discussions in small groups (four to nine people per group) of mixed gender . Recruitment rate will be recorded as a percentage, e.g. 25% (48/195). It is acknowledged that advertisements may reach a large number of individuals but it is impossible to quantify this;Retention rate will be calculated as the number of participants completing the study as a proportion of those randomised;The appropriateness, practicality and acceptability of the designed intervention in the proposed settings will be assessed using focus group interviews. The focus group transcripts will be analysed using a phenomenological inductive approach , and theThe assessment rate of questionnaires will be evaluated as the total number of completed questionnaires divided by the total number of questionnaires and recorded as a percentage;The appropriateness of statistical methods of data analysis will be analysed by the research team in terms of suitability to the data;A power calculation and sample size estimation will be calculated for meaningful potential future primary outcomes  using a method based on the differences in means between the intervention and control groups, using the G-power software ;The effect size (ES) will be calculated for loneliness, social support, social networks, anxiety and depression, self-efficacy for exercise, satisfaction with level of social contacts and the expected outcomes and barriers for exercise. Means (M) and standard deviations (SD) will be used to investigate the effect size for change in loneliness using mixed between (intervention group vs. control group) and within (over time) repeated-measures analysis of variance (ANOVAs) with post-hoc comparisons.The following feasibility outcomes will be assessed in this study:The data monitoring committee for this project is the supervisory research team (three academic supervisors) who are independent of the trial sponsor. They will be responsible for checking data accuracy upon assembly of the final database following completion of data collection prior to data analysis. The PhD student is responsible for monitoring and reporting spontaneous adverse events or any unintended trial effects to the supervisory team, and the PI (Whittaker) is responsible for reporting these to the sponsor. The trial is also subject to independent audit request by the sponsor, the University of Birmingham, by a team independent to the supervisory/research team. The sponsor contact details are given below.Dr Sean Jennings, Head of Research Governance, University of Birmingham (e-mail: s.jennings@bham.ac.uk).Data will be collected at the university facility at screening, baseline and post-intervention period (12 weeks after the start of the intervention)  and communities in Birmingham via a two-step strategy. First, through widespread advertisement in local media resources , via posters  who is the researcher.Recruitment will be aimed to be at a rate of 10 participants a month (to a minimum of 40 participants) for estimation of the critical parameters of the feasibility study . If the As suggested by El-Kotob et al. , feasibip < .05; however, any hypothesis testing is preliminary and results will be interpreted with caution as this pilot study is underpowered and the analyses based on small numbers. Baseline differences between groups for continuous data  will be analysed using one-way ANOVA. Chi-squared tests will be applied for nominal data . For descriptive statistics, data will be presented as means (M) and standard deviations (SD). Nominal data will be presented as number (N) and percentage. Mixed between (group) and within (time) repeated-measures ANOVAs with post-hoc comparisons will be applied to investigate the effect of the intervention versus control on psychosocial outcomes , expected outcomes and barriers for exercise, and accelerometer data. The accelerometer data will be analysed using the ActivPAL software V7.1.18 . Recorded data will be downloaded to a computer, and data for average daily amount of stepping (step counts), average time lying and sitting (h) in increments of 15 s, average time standing (h) and energy expenditure  will be analysed using mixed between (intervention group) and within (time) ANOVAs. For the Expected Outcomes and Barriers for Habitual Exercise scale [Quantitative data will be analysed using SPSS version 22.0 for Windows  employing an intention-to-treat analysis  . The levse scale , additiose scale .Qualitative data will be thematically analysed by two research team members independently using a phenomenological inductive approach . TranscrResearch data will be kept for 10 years in line with UK data protection regulations . PhysicaThis study will explore the feasibility and acceptability of the Physical Activity Intervention for Loneliness (PAIL) in community-dwelling older adults. The key features of the study are based on effective components of physical activity interventions to treat loneliness obtained from a systematic review and meta-analysis of the existing evidence that highlighted the lack of physical activity interventions for older adults residing in community settings . Studieswww.campaigntoendloneliness.org. The published study protocol and journal paper will be uploaded to the Current Research Information System (CRIS) – PURE portal available at pure.bham.ac.uk open to the academic staff, students and external researchers.Dissemination will include a summary of the results for participants, publication of an open-access article detailing the full findings  and presentation of study findings at conferences. The results of this feasibility study will be used to inform the development of a large-scale RCT. Participants in the intervention will be sent a feedback information sheet with the results of the study by e-mail or post (if no e-mail is available). The findings are also intended to be disseminated at the British Geriatric Society Loneliness annual meetings and via news at Clinicaltrials.gov Identifier: NCT03458793. Ethical approval for the project was received from the STEM Research Ethics Committee of the University of Birmingham, UK (ERN_16-1419A). Participant recruitment and baseline data collection started in October 2017 and is expected to be finished by November 2018. The study will be fully completed by May 2019.This feasibility trial is registered at Additional file 1:Consent form (DOCX 61 kb)Additional file 2:The SPIRIT 2013 Checklist (DOCX 122 kb)Additional file 3:Phone-based eligibility screening form (DOCX 47 kb)Additional file 4:Content of group workshops (DOCX 17 kb)Additional file 5:Mid-point focus group questions (DOCX 24 kb)Additional file 6:End-point focus group questions (DOCX 18 kb)Additional file 7:Information sheet (DOCX 282 kb)Additional file 8:Recruitment poster (DOCX 359 kb)


A preliminary exploratory study shows solid agreement between the results of case reports and clinical study meta-analyses in mucopolysaccharidosis Type I (MPS-I) adult patients. The aim of the present study is to confirm previous results in another patient population, suffering from mucopolysaccharidosis Type II (MPS-II).A systematic review and meta-analysis of case reports published by April 2018 was conducted for MPS-II patients treated with enzyme replacement therapy (ERT). The study is reported in accordance with PRISMA and MOOSE guidelines (PROSPERO database code CRD42018093408). The assessed population and outcomes were the same as previously analyzed in a meta-analysis of MPS-II clinical studies. The primary endpoint was the percent of clinical cases showing improvement in efficacy outcome, or no harm in safety outcome after ERT initiation. A restrictive procedure to aggregate case reports, by selecting standardized and well-defined outcomes, was proposed. Different sensitivity analyses were able to evaluate the robustness of results.Every outcome classified as “acceptable evidence group” in our case report meta-analysis had been graded as “moderate strength of evidence” in the aforementioned meta-analysis of clinical studies. Sensitivity, specificity, and positive-negative predictive values for results of both meta-analyses reached 100%, and were deemed equivalent.Aggregating case reports quantitatively, rather than analyzing them qualitatively, may improve conclusions in rare diseases and personalized medicine. Additionally, we propose some methods to evaluate publication bias and heterogeneity of the included studies in a meta-analysis of case reports. The low prevalence of rare diseases, the phenotype heterogeneity and the long latency period, may prevent and/or make the possibility of performing randomized clinical trials (RCTs) and large studies extremely difficult , 2. TherPrevious FDA drug approvals with breakthrough status suggest that sometimes non-controlled studies can provide the same quality of evidence to demonstrate a positive risk–benefit ratio as individual RCTs , 4. AccoIn rare diseases, research based on registries and case studies is likely the best option, due to lack of patients, and case reports are often the primary evidence of the effectiveness of a new therapy or treatment . Due to Case report databases are developed as the Consensus-based Clinical Case Reporting (CARE) guidelines  attempt A previous systematic review of clinical studies, evaluating the effectiveness of enzyme replacement therapy (ERT) in adults (≥ 18 years) with mucopolysaccharidosis Type I (MPS-I), rated the strength of evidence (SOE) for ERT on each outcome with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria . AnotherOur proposal performed a meta-analysis of case reports of MPS-II patients treated with ERT, and compared the degree of evidence assigned to each outcome, vs. what was assigned in a previous clinical study meta-analysis, published by an independent research group. In a population suffering from MPS-II, we sought to confirm the impressive rate of agreement seen between case reports and clinical study meta-analyses in patients with MPS-I .A systematic review of case reports published through April 2018 was conducted for MPS-II patients treated with ERT. It was carried out on EMBASE, MEDLINE, The Cochrane Library , Cochrane Methodology Register and Health Technology Assessment Databases) as well as on the Latin American and Caribbean Literature on Health Sciences (LILACS). The search strategy retrieved citations in databases containing the subject headings: Hunter syndrome, enzyme replacement, iduronate 2 sultatase, idursulfase, case report, case study, and medical record review. The search terms were adapted and used with different bibliographic databases  entered findings into a database, independently reviewed citations/abstracts from the database and hand searches, and selected full relevant articles and documents for data extraction using preset criteria. Discrepancies were resolved through discussion or input from a third reviewer (PLJ).In the meta-analysis of MPS-II clinical studies by Bradley et al. , each ouUrinary glycosaminoglycans (uGAGs) level (μg/mg creatinine) reduction -- moderate.Liver volume -- moderate.Harm: development of antibodies -- moderate.Harm: Rate of IRRs (infusion-related reactions) and SAEs (serious adverse events) -- low.6-min walk test in meters (6MWT) -- low.Growth: height -- low.Pulmonary function , normalized for age and sex --low.Joint range of motion (JROM) -- insufficient.Benefit and harm: physical disability/quality of life -- insufficient.Cardiac function -- insufficient.Sleep apnea -- insufficient.The long-term outcomes were not ranked with SOE . AuthorsOur meta-analysis of case reports considered the same outcomes analyzed in Bradley’s study . We alsoFor secondary endpoints, the improvement in ERT was weakly defined; we assumed the existence of improvement with the mention of improvement in the case report (weak confirmatory method).p-value was equal to or lower than the FDR 10% critical value, which was calculated by ranking the outcomes from the lowest to the highest p-value. The FDR critical value is defined as the rank (r) divided by number of outcomes (nr), and multiplied by the accepted 10% rate of false discoveries. Outcomes not achieving this were classified as unacceptable evidence.The primary endpoint was the percentage of case reports with an outcome modification after ERT, based on a strong confirmatory method. This percentage was calculated for each of the 11 outcomes analyzed. Additionally, we classified these outcomes in two groups (acceptable or unacceptable evidence), based on the percentage of case reports. To classify them, we tested the null hypothesis for the percentage of case reports with an outcome modification lower or equal to 5% (H0). We based our analysis on a one-sided binomial test. Multiplicity issues, derived from analyzing 11 outcomes  were adjWe evaluated the concordance between outcomes in the acceptable evidence group, based on the primary outcome and outcomes classified as high to moderate SOE in the Bradley meta-analysis . The valThe primary analysis was performed in the primary analysis set, which was considered in all case reports of MPS-II patients, treated with ERT with reported efficacy and safety. These case reports were written in narrative form (results were not aggregated) and published prior to the bibliographic search by Bradley et al. .The secondary analysis evaluated concordance between outcomes in the acceptable evidence group, with a weak confirmatory assumption, and outcomes classified as high to moderate SOE in the Bradley meta-analysis . ValiditSecondary analysis evaluated the relative agreement between number of case reports showing a modification in a specific outcome and the SOE score  reported from the clinical study meta-analysis. Spearman rank correlation was used to evaluate relative agreement. Results were presented in a scatter plot.Including all case reports, even those published after the bibliographic search by Bradley et al. (31 December 2015) .Excluding all studies that had been analyzed in the Bradley study.Excluding all congress communications.Sensitivity analysis was planned in three ways. First, we compared the measures of validity and relative agreement with strong and weak confirmatory methods in the primary analysis set. Second, we estimated measures of validity and relative agreement derived with a strong confirmatory assumption in three analysis sets:We compared the validity indices based on different assumptions about the futility boundary (null hypothesis): 5% primary analysis-, 1, 10, 15, and 20%. The analyses were performed in the primary analysis set.Database searches through April 13th, 2018 identified 331 citations and 289 unique abstracts. The reference of all abstracts screened and the reason for exclusion are reported in the Additional file Table p-value lower than the FDR 10% critical value were classified in the acceptable evidence group. These outcomes were uGAGs, liver volume and development of antibodies; they had been also classified as having moderate quality of evidence by SOE criteria in the previous meta-analysis of clinical studies. Additionally, all outcomes classified in the unacceptable evidence group  had been classified as having low to insufficient quality of evidence by the SOE criteria in the Bradley study . In addition, the ratio of agreement between number of case reports with improved outcomes and the SOE score was good (Rho > 80%).When we excluded congress communications from the analysis set, the accuracy between our classification (based on the strong confirmatory method) and the SOE classification was reduced to 91%, there was no detection of the development of antibodies as modified by ERT in our meta-analysis Table .Table 4The best validity indices were observed with the preplanned futility boundary . Specificity and positive predictive values were reduced with boundaries lower than 5%. Sensitivity and negative predictive values were reduced with boundaries higher than 5% , has been collecting information on patients with MPS II for over 10 years . Our resRegarding IRRs, which our analysis categorized as unacceptable evidence, we only considered as relevant the IRRs that caused ERT dose changes. Data from the HOS registry showed that although 32% of patients suffer from IRRs, most patients (85%) experience them during the first 3 months of treatment and most IRRs are mild or moderate in severity and can be managed without interrupting treatment .An important point to consider is the futility boundary selected in our analysis (null hypothesis). The objective of Bradley et al.  and theiWe have demonstrated that standardization and a good definition of outcomes evaluated in case reports are strongly related with the validity of the results obtained based on their aggregation . Thus, eDifferent authors have underlined the impact of clinical report results in clinical practice and research , while cClinical reports have a high risk of publication bias  and it iAnother important limitation is that we cannot estimate the effect size of an outcome. Nevertheless, we observed that most clinical cases do not report enough information to aggregate study results in a mean, median, or a proportion with a confidence interval. This highlights the importance of initiatives to homogenize and upgrade the quality of the information published in case reports . AdditioThis analysis was confirmed in a MPS-II population treated with ERT, with results explored in a specific group of MPS-I patients. New studies must assess if results can be generalized for other diseases and patient profiles.A meta-analysis of clinical reports cannot replace evidence provided by clinical trials. Subject recruitment in rare diseases and personalized medicine represent a critical task in clinical research , 43, 44.We demonstrated the agreement between the results from case reports and clinical studies based on meta-analyses, which evaluate efficacy and safety of enzyme replacement therapy in patients with MPS-II. These results confirm previous results observed in MPS-I adult patients.We suggest that quantitatively combining results from case reports with standardized and well-defined outcomes, rather than analyzing them separately or qualitatively, may improve clinical evidence of the effect of a therapeutic strategy.Additionally, we have proposed some methods to evaluate publication bias and heterogeneity of the included studies in a meta-analysis of case reports.Case reports meta-analyses might help improving the clinical practice and the design of clinical trials in the context of rare diseases and increasingly in other areas of personalized medicine.Additional file 1: Table S1. Search syntax used in different databases to gather the bibliographic data. Table S2. Case reports of males with MPS-II published prior to the bibliographic search of meta-analysis of clinical studies (January 2008 to December 2015). Table S3. Case reports of males with MPS-II published later to the bibliographic search of the meta-analysis of clinical studies (January 2016 to April 2018). Table S4. Agreement between the classification of outcomes based on the case report meta-analysis and the SOE classification based on the clinical study meta-analysis. Weak confirmatory method. Table S5. Sensitivity analysis on different futility boundaries. Figure S1. Agreement between the score of evidence from the case report meta-analysis and SOE from the clinical study meta-analysis. Weak confirmatory method.Additional file 2. This file includes all the references screened in the systematic review and the reason for exclusion.


Earlier studies also showed that atypical duct-like structures in the pancreas developed age-dependently in Tg mice. Numerous reports have described that karyopherin alpha 2 (KPNA2) is highly expressed in cancers of different kinds. However, details of the expression of KPNA2 in pancreatic ductal atypia and in normal pancreatic tissues remain unclear. To assess the feature of the expression of KPNA2 in the development of the ductal atypia and islet architectures, we scrutinized the pancreas of Tg mice histopathologically. Results showed that considerable expression of KPNA2 was observed in pancreatic β-cells, suggesting its importance in maintaining the functions of β-cells. In mature stages, the level of KPNA2 expression was lower in islets of Tg mice than in wild-type controls. At 4 weeks, the expression levels of KPNA2 in islets of Tg mice were the same as those in wild-type controls. These results suggest that the reduction of KPNA2 might contribute to β-cell dysfunction in mature Tg mice. Additionally, the formation of mucin-producing intra-islet ducts, islet fibrosis, and massive T cell recruitment to the islet occurred in aged Tg mice. In exocrine areas, primary pancreatic intraepithelial neoplasias (PanINs) with mucinous pancreatic duct glands (PDGs) emerged in aged Tg mice. High expression of KPNA2 was observed in the ductal atypia. By contrast, KPNA2 expression in normal ducts was quite low. Thus, upregulation of KPNA2 seemed to be correlated with progression of the degree of atypia in pancreatic ductal cells. The SASP-like microenvironment inside islets might play stimulatory roles in the formation of ductal metaplasia inside islets and in islet fibrosis in Tg mice.Our earlier studies demonstrated that cysteine414- (zinc-binding site of mCRY1-) alanine mutant mCRY1 transgenic mice (Tg mice) exhibit diabetes characterized by the reduction of  We clμm) were conducted mainly as described in earlier reports [Immunohistochemical analyses using paraffin-embedded pancreas sections  with the aged and late middle-aged ones. The expression level of KPNA2 in the nucleus of cells in islets at 4 weeks as a whole was remarkably lower than that of the mature stage . In the α-cells were distributed abnormally over the islet  are induced during regeneration of pancreatic tissue after pancreatectomy and have been proposed as growth factors for pancreatic beta cells . We examondition  and induondition –29. Our s in TGs . Our respancreas .ß-cells. They seem to function in roles as stimulators in developing the intra-islet ductal structures in TGs  . These ss in TGs . The recterature , 32.Our results suggest that activated PSCs play no major role in forming fibrosis in the islet of TGs. Consequently, it is likely that the SASP-like phenotype of islets of TGs itself is a prime cause for the islet fibrosis, which progresses with age. Results suggest that the development of PanIN and PDG is associated with islet inflammation using a human-IAPP-transgenic (HIP) rat model . ConsideThe results presented herein suggest that, in TGs, the reduction of KPNA2 expression in the islet might contribute to anomalies of the islet function in mature stages. Furthermore, a possible correlation was found between KPNA2 expression and the generation of ductal metaplasia. We demonstrated with overexpressing C414A-CRY1 in mice that fibrosis of islets accompanying ductal metaplasia in islets and the attraction of immune cells near islets occurred age-dependently. Taken together, our results have revealed some new aspects of islet pathophysiology, providing clues to understanding the development of ductal metaplasia. Our results indicate that the upregulation of KPNA2 can be a useful barometer for pancreatic ductal cells with atypical features. The molecular importance of the increment of KPNA2 in the formation of potentially precancerous ductal lesions remains to be elucidated.


Pyrazinamide (PZA) is a key component of current and future regimens for tuberculosis (TB). Inclusion of PZA at higher doses and for longer durations may improve efficacy outcomes but must be balanced against the potential for worse safety outcomes.p values. Meta-analysis will be performed using the generalised inverse variance method for fixed effects models (FEM) or the DerSimonian-Laird method for random effects models (REM). For indirect comparisons, meta-regression for absolute measures against dose and duration data will be performed. Heterogeneity will be quantified through the I2-statistic for direct comparisons and the τ2 statistic for indirect comparisons using meta-regression.We will search for randomised and quasi-randomised clinical trials in adult participants with and without the inclusion of PZA in TB treatment regimens in the Cochrane infectious diseases group’s trials register, Cochrane central register of controlled trials (CENTRAL), MEDLINE, EMBASE, LILACS, the metaRegister of Controlled Trials (mRCT) and the World Health Organization (WHO) international clinical trials registry platform. One author will screen abstracts and remove ineligible studies (10% of which will be double-screened by a second author). Two authors will review full texts for inclusion. Safety and efficacy data will be extracted to pre-piloted forms by one author (10% of which will be double-extracted by a second author). The Cochrane risk of bias tool will be used to assess study quality. The study has three objectives: the association of (1) inclusion, (2) dose and (3) duration of PZA with efficacy and safety outcomes. Risk ratios as relative measures of effect for direct comparisons within trials  and proportions as absolute measures of effect for indirect comparisons across trials (for objectives 2 and 3) will be calculated. If there is insufficient data for direct comparisons within trials for objective 1, indirect comparisons between trials will be performed. Measures of effect will be pooled, with corresponding 95% confidence intervals and The current use of PZA for TB is based on over 60 years of clinical trial data, but this has never been synthesised to guide rationale use in future regimens and clinical trials.Systematic review registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019138735 It is considered an essential first-line drug and may also be used as part of treatment for multi-drug resistant disease (MDR TB). First introduced in the 1950s, its mechanism of action remains poorly understood and appears to be highly dependent on ambient pH at the site of action. PZA may be a pro-drug, undergoing conversion in vivo to pyrazinoic acid (POA) [A to POA . InitialA to POA . SubsequA to POA . In multA to POA . In patiA to POA . At leasA to POA , 9. InteA to POA , 11. DesA to POA  and madeA to POA . We hopeInclusion of PZADose of PZADuration of PZAWe aim to determine whether the following are associated with improved efficacy or worse safety outcomes during treatment of tuberculosis using first- or second-line regimens:This review will be conducted in accordance with the PRISMA statement ; a complFor objective 1 (inclusion vs. non-inclusion of PZA): anti-tuberculosis treatment regimens which do not contain PZA .For objective 2 (dose of PZA): anti-tuberculosis treatment regimens containing PZA at different doses.For objective 3 (duration of PZA): anti-tuberculosis treatment regimens in which PZA is administered for different durations .For efficacy outcomes, we will include randomised controlled trials and quasi-randomised controlled trials in adults with pulmonary tuberculosis  or mycobacterial culture) whether fully drug-sensitive, isoniazid-resistant or MDR-TB. For safety outcomes, we considered that a microbiological diagnosis was not required, and so trials with clinical and extra-pulmonary diagnoses of TB will be included in addition. To address the objectives of this study, trials will be included if they include anti-tuberculosis regimens containing PZA and/or allow at least one of the following three comparisons:Efficacy and safety outcomes will be assessed as follows:Treatment failure—mycobacterial culture positive at completion of treatment protocolRelapse—mycobacterial culture-negative at completion of treatment protocol but culture-positive during post-treatment follow-up periodPrimary–efficacy (as per reference )TreatmeAdverse eventsSerious adverse events (as defined by the investigators)Discontinuation of PZARegimen switchDeaths Deaths (due to tuberculosis)Primary–safetyInvestigator-defined drug-induced liver injury (DILI) Arthralgia and joint effusionsSecondary–safetyThe Cochrane Library), MEDLINE (1950 to December 2019), EMBASE (1975 to December 2019) and LILACS (1982 to December 2019). The search strategy is designed to be inclusive and identify all eligible clinical trials in tuberculosis with or without the inclusion of PZA as follows.Tuberculosis AND clinical trialsRifampicin OR isoniazid OR pyrazinamide OR ethambutol OR thiacetazone OR para-aminosalicylic acid OR streptomycin OR rifabutin OR rifapentine OR levofloxacin OR ofloxacin OR gatifloxacin OR moxifloxacin OR bedaquiline OR pretomanid1 AND 2We will search for studies meeting the eligibility criteria in the Cochrane infectious diseases group’s trials register (2019), Cochrane central register of controlled trials  and the World Health Organization (WHO) international clinical trials registry platform using ‘tuberculosis’ as the search term. We will not exclude studies based on language of publication. All studies regardless of publication status  will be eligible. We will hand search the reference lists of included studies and relevant reviews to identify other potentially eligible studies.A complete list of studies identified by the search strategy will be compiled and duplicates removed. One author (AM) will review the abstracts of all identified studies and remove ineligible studies, with 10% of abstracts being double-screened by a second author (JM). The full text for remaining potentially eligible studies will be accessed and reviewed by two authors (AM and JM) for inclusion. Any disagreements on eligibility will be resolved by discussion or referral to a third author (GD). Efficacy data has already been independently extracted by two authors (LB and GD) until November 2016 using a standardised electronic data extraction form. Data was collected, and inconsistent data recording was resolved through discussion and contacting the author where necessary. This search and extraction will be updated until December 2019 following the same methodology by a single author (AM), with 10% double-extracted by a second author (JM) to check for consistency. Additional safety data for all studies will be extracted to a pre-piloted supplementary electronic data extraction form for more detailed safety outcomes by a single author (AM) and 10% double-extracted by a second author (JM). We will collect data on study design, included participants, treatment regimens, outcomes and selected other data. Study authors will be contacted when the study report does not provide sufficient information concerning the study methods, treatment effect sizes or numbers of outcomes for each treatment arm. All extracted data will be entered into a spreadsheet.Bias will be assessed using the Cochrane risk of bias tool to assess the following source of bias domains: (1) selection bias  via assessment of adequate sequence generation in the allocation of participants to intervention and control groups and adequate allocation concealment, (2) performance bias via assessment of blinding of personnel and participants to treatment allocation, (3) detection bias via assessment of blinding of the outcome assessor to treatment allocation, (4) reporting bias via assessment of selective outcome reporting, (5) attrition bias via assessment of the description of participants not included versus those included in the final analysis.p values will be calculated; results will be deemed statistically significant at p < 0.05. Meta-regression will be performed for absolute measures against categorised dose and duration data, including their interaction. For direct comparisons, we will quantify the level of the observed heterogeneity through the I2-statistic. For indirect comparisons using meta-regression, the τ2 statistic will be used to assess heterogeneity. We will perform the following sensitivity analyses where appropriate: (1) intention to treat , (2) per protocol , (3) worst-case scenario (where participants missing an outcome are assumed to have failed or relapsed for efficacy outcomes or had a negative safety outcome) and (4) best-case scenario (where participants missing an outcome are assumed to have favourable efficacy and safety outcomes).We will calculate risk ratios as relative measures of effect for direct comparisons within trials  and proportions as absolute measures of effect for indirect comparisons across trials (for objectives 2 and 3 only). If there is insufficient data for direct comparisons within trials for objective 1, indirect comparisons between trials will be performed. Statistical analysis will be conducted using R for Windows, version 3.6.0. Relative and absolute measures of effect will be pooled and analysed using the package metafor. Meta-analysis will be performed using the generalised inverse variance method for fixed effects models (FEM) or the DerSimonian-Laird method for random effects models (REM) . Forest HIV co-infected versus non-infected participantsGeographical location of study Symptomatic versus asymptomatic DILIDILI defined as alanine aminotransferase (ALT): aspartate aminotransferase (AST) ratio > 5 vs. ALT:AST ratio > 3Contingent on sufficient studies, we plan to explore the following subgroups:I2 value is over 40% and there are sufficient studies, we will explore sources of heterogeneity by stratifying on study-level characteristics including average number of drugs included in treatment regimens, use of fluoroquinolones, use of rifampicin, average number of drugs to which isolates show resistance, number of patients previously treated for TB, extent of disease and study quality.If the Funnel plots and Galbraith plots will be produced for all outcomes where numbers of trials (> 10) permit.This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO); number CRD42019138735.This systematic review and meta-analysis will be the first, to our knowledge, to examine the trade-off between efficacy and safety in the use of PZA in TB, irrespective of resistance pattern. PZA is an old TB drug, but one that is likely to remain important in current and future regimens for TB and has been used in a wealth of clinical trials over the last 60 years. Correctly synthesised, this data provides the opportunity for the more rational use of PZA in future regimens and clinical trials, and hence this review has the potential for wide application. The principal practical and operational issues we foresee in this systematic review and meta-analysis of clinical trials spanning many years of publication are poor quality trials and heterogenous interventions and outcome reporting . NonetheAdditional file 1. PRISMA-P checklist.