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Pericytes facilitate blood–brain barrier (BBB) integrity; however, the mechanisms involved remain unclear. Hence, using co-cultures of human cerebral microvascular endothelial cells (ECs) and vascular pericytes (PCs) in different spatial arrangements, as well as PC conditioned media, we investigated the impact of PC-EC orientation and PC-derived soluble factors on EC barrier function. We provide the first evidence that barrier-inducing properties of PCs require basolateral contact with ECs. Gene expression analysis (GEA) in ECs co-cultured with PCs versus ECs alone showed significant upregulation of 38 genes and downregulation of 122 genes. Pathway enrichment analysis of modulated genes showed significant regulation of several pathways, including transforming growth factor-β and interleukin-1 regulated extracellular matrix, interferon and interleukin signaling, immune system signaling, receptor of advanced glycation end products (RAGE), and cytokine–cytokine receptor interaction. Transcriptomic analysis showed a reduction in molecules such as pro-inflammatory cytokines and chemokines, which are known to be induced during BBB disruption. Moreover, cytokine proteome array confirmed the downregulation of key pro-inflammatory cytokines and chemokines on the protein level. Other molecules which influence BBB and were favorably modulated upon EC-PC co-culture include IL-18 binding protein, kallikrein-3, CSF2 CSF3, CXCL10, CXCL11 (downregulated) and IL-1-R4; HGF, PDGF-AB/BB, PECAM, SERPIN E1 (upregulated). In conclusion, we provide the first evidence that (1) basolateral contact between ECs and PCs is essential for EC barrier function and integrity; (2) in ECs co-cultured with PCs, the profile of BBB disrupting pro-inflammatory molecules and cytokines/chemokines is downregulated; (3) PCs significantly modulate EC mechanisms known to improve barrier function, including TGF-β regulated ECM pathway, anti-inflammatory cytokines, growth factors and matrix proteins. This human PC-EC co-culture may serve as a viable in vitro model for investigating BBB function and drug transport. [ABSTRACT FROM AUTHOR]

Unsupervised machine learning and knowledge discovery from large-scale datasets have recently attracted a lot of research interest. The present paper proposes a distributed big data clustering approach-based on adaptive density estimation. The proposed method is developed-based on Apache Spark framework and tested on some of the prevalent datasets. In the first step of this algorithm, the input data is divided into partitions using a Bayesian type of Locality Sensitive Hashing (LSH). Partitioning makes the processing fully parallel and much simpler by avoiding unneeded calculations. Each of the proposed algorithm steps is completely independent of the others and no serial bottleneck exists all over the clustering procedure. Locality preservation also filters out the outliers and enhances the robustness of the proposed approach. Density is defined on the basis of Ordered Weighted Averaging (OWA) distance which makes clusters more homogenous. According to the density of each node, the local density peaks will be detected adaptively. By merging the local peaks, final cluster centers will be obtained and other data points will be a member of the cluster with the nearest center. The proposed method has been implemented and compared with similar recently published researches. Cluster validity indexes achieved from the proposed method shows its superiorities in precision and noise robustness in comparison with recent researches. Comparison with similar approaches also shows superiorities of the proposed method in scalability, high performance, and low computation cost. The proposed method is a general clustering approach and it has been used in gene expression clustering as a sample of its application. [ABSTRACT FROM AUTHOR]