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In this study, we measured the enzymatic activity of acetylcholinesterase (AChE) using the Light Addressable Amperometric Sensor (LAAS), an electrochemical sensor that can easily measure redox current values of multiple samples. Acetylthiocholine (ATCh) was used as a substrate. ATCh is hydrolyzed by AChE and releases electrons, making it possible to measure current values in accordance with the amount of substrate using LAAS. When KCl solution was used as the supporting electrolyte and an Ag/AgCl reference electrode as the counter electrode, a correlation between substrate concentration and reaction was confirmed in the range of 1 μM–10 mM. Malathion was then used as an inhibitor of AChE. Malathion phosphorylates and inactivates AChE, which prevents substrate hydrolysis and is expected to decrease the current value. Experimental results showed a decrease in sensor response correlated with inhibitor concentration. [ABSTRACT FROM AUTHOR]

R, an open source and open development software project based on the R programming language for analysing and comprehending genetic data, is one of the most well-liked platforms. The workflow of analysing microarray data is shown in this work using the R analysis package, from different types of annotation, normalisation, expression index calculation, diagnostic plots, to pathway analysis for meaningful data visualisation and interpretation. Aim of the study was to analyse the microarray data of pediatric versus adult cornea using R software. Published microarray data gene expression patterns of pediatric and adult cornea were obtained from ncbi geo database. Integrated bioinformatics tools were used to analyze and compare the gene expression patterns. Data were processed using the software R, function and pathway enrichment analysis of differentially expressed genes (DEGs) was carried out using Gene ontology (GO) and KEGG database, protein protein interaction was studied by string database. A total of 33,297 differentially expressed genes (DEGs) were studied. There were no statistically significant differences in any gene between the groups. Genes enriched by more than 1.5 times were studied in more detail. Of the 20 major genes expressed, 11 were down-regulated and 9 were up-regulated. Volcano, mean difference plot, box plot, histogram, Venn's diagram compared the differentially expressed genes. None of the genes were significantly upregulated/downregulated/enriched in both the groups. On string analysis, 34 nodes, 8 edges were found. Average node degree was 0.471, average local cluster coefficient was 0.191 and p value for PPI enrichment was 0.326. The study displays the effectiveness of bioinformatics analysis methods in screening potential genes expressed in pediatric versus adult cornea using R software. [ABSTRACT FROM AUTHOR]

Cancer is most dangerous disease and most of the deaths are because of this deadly disease. Microarray cancer analysis is currently one of the most popular study topics in machine learning, computational biology. In the case of binary classes, classification and analysis of cancer data play a critical role in diagnosis, distinguishing negative and positive cases and treatment. The curse of dimensionality and a lack of sufficient sample data are the key challenges with microarray cancer datasets. In this paper presents, problems associated with micro array and handling of problems and make datasets ready for analysis. Different classification algorithms explained with pros and cons of each algorithm in detail. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease is the most common hepatic disease in western countries and is even more ubiquitous in Asian countries. Our study determined that TH17/Treg cells were imbalanced in animal models. Based on our interest in the mechanism underlying TH17/Treg cell imbalance in nonalcoholic fatty liver mice, we conducted a joint bioinformatics analysis to further investigate this process. Common gene sequencing analysis was based on one trial from one sequencing platform, where gene expression analysis and enrichment analysis were the only analyses performed. We compared different sequencing results from different trials performed using different sequencing platforms, and we utilized the intersection of these analytical results to perform joint analysis. We used a bioinformatics analysis method to perform enrichment analysis and map interaction network analysis and predict potential microRNA sites. Animal experiments were also designed to validate the results of the data analysis based on quantitative polymerase chain reaction (qPCR) and western blotting. Our results revealed 8 coexisting differentially expressed genes (DEGs) and 7 hinge genes. The identified DEGs may influence nonalcoholic steatosis hepatitis through the interleukin-17 pathway. We found that microRNA-29c interacts with FOS and IGFBP1. Polymerase chain reaction analyses revealed both FOS and microRNA-29c expression in NASH mice, and western blot analyses indicated the same trend with regard to FOS protein levels. Based on these results, we suggest that microRNA-29c acts on FOS via the interleukin-17 signaling pathway to regulate TH17/Treg cells in NASH patients.

Nonalcoholic fatty liver disease is the most common hepatic disease in western countries and is even more ubiquitous in Asian countries. Our study determined that TH17/Treg cells were imbalanced in animal models. Based on our interest in the mechanism underlying TH17/Treg cell imbalance in nonalcoholic fatty liver mice, we conducted a joint bioinformatics analysis to further investigate this process. Common gene sequencing analysis was based on one trial from one sequencing platform, where gene expression analysis and enrichment analysis were the only analyses performed. We compared different sequencing results from different trials performed using different sequencing platforms, and we utilized the intersection of these analytical results to perform joint analysis. We used a bioinformatics analysis method to perform enrichment analysis and map interaction network analysis and predict potential microRNA sites. Animal experiments were also designed to validate the results of the data analysis based on quantitative polymerase chain reaction (qPCR) and western blotting. Our results revealed 8 coexisting differentially expressed genes (DEGs) and 7 hinge genes. The identified DEGs may influence nonalcoholic steatosis hepatitis through the interleukin-17 pathway. We found that microRNA-29c interacts with FOS and IGFBP1. Polymerase chain reaction analyses revealed both FOS and microRNA-29c expression in NASH mice, and western blot analyses indicated the same trend with regard to FOS protein levels. Based on these results, we suggest that microRNA-29c acts on FOS via the interleukin-17 signaling pathway to regulate TH17/Treg cells in NASH patients. [ABSTRACT FROM AUTHOR]

Gene Expression Analysis seeks to find the highly expressive genes from a highly dimensional Microarray disease gene Database by using some statistical gene selection approaches based on supervised or unsupervised learning. Gene Ontology (GO) introduces a series of method for annotating gene function that combines semantic similarity measures by taking account on the underlying topology of gene interaction networks for structuring the graphs of the gene ontology. Initially, the genes are identified by clustering microarray disease dataset giving gene id of most expressive genes and further the genes are associated based on their biological functionalities using the gene ontology annotations taken from bioinformatics database. Also, t-test is used for finding the up-regulated genes so it can be annotated to find the most significant gene terms in hierarchical graph structure. The proposed method uses term Similarity measures to compare two or more gene ontology terms. Finally, gene functional classification and gene term association is done by forming a graph structure to be readily analysed by medical practitioner intending the nature of disease-causing genes at deeper level of understanding in chronic disorder based health care environments.

Gene Expression Analysis seeks to find the highly expressive genes from a highly dimensional Microarray disease gene Database by using some statistical gene selection approaches based on supervised or unsupervised learning. Gene Ontology (GO) introduces a series of method for annotating gene function that combines semantic similarity measures by taking account on the underlying topology of gene interaction networks for structuring the graphs of the gene ontology. Initially, the genes are identified by clustering microarray disease dataset giving gene id of most expressive genes and further the genes are associated based on their biological functionalities using the gene ontology annotations taken from bioinformatics database. Also, t-test is used for finding the up-regulated genes so it can be annotated to find the most significant gene terms in hierarchical graph structure. The proposed method uses term Similarity measures to compare two or more gene ontology terms. Finally, gene functional classification and gene term association is done by forming a graph structure to be readily analysed by medical practitioner intending the nature of disease-causing genes at deeper level of understanding in chronic disorder based health care environments. [ABSTRACT FROM AUTHOR]

Microarray data and sequencing data are two main types of high-throughput data in cancer related study. The most obvious advantages of microarray data are its sensitivity, rapid detection, and relatively low cost. Due to the fact that microarray data could only identify known genes instead of novel genes and variants, sequencing data have been involved in a lot of cancer mechanism studies. In general, both types of data are crucial in cancer research. Along with development of precision medicine, high-throughput data analysis can help people understand the mechanisms of cancer as well as expedite personalized cancer treatment. [ABSTRACT FROM AUTHOR]

As long as the genetically modified crops are gaining attention globally, their proper approval and commercialization need accurate and reliable diagnostic methods for the transgenic content. These diagnostic techniques are mainly divided into two major groups, i.e., identification of transgenic (1) DNA and (2) proteins from GMOs and their products. Conventional methods such as PCR (polymerase chain reaction) and enzyme-linked immunosorbent assay (ELISA) were routinely employed for DNA and protein based quantification respectively. Although, these Techniques (PCR and ELISA) are considered as significantly convenient and productive, but there is need for more advance technologies that allow for high throughput detection and the quantification of GM event as the production of more complex GMO is increasing day by day. Therefore, recent approaches like microarray, capillary gel electrophoresis, digital PCR and next generation sequencing are more promising due to their accuracy and precise detection of transgenic contents. The present article is a brief comparative study of all such detection techniques on the basis of their advent, feasibility, accuracy, and cost effectiveness. However, these emerging technologies have a lot to do with detection of a specific event, contamination of different events and determination of fusion as well as stacked gene protein are the critical issues to be addressed in future.

As long as the genetically modified crops are gaining attention globally, their proper approval and commercialization need accurate and reliable diagnostic methods for the transgenic content. These diagnostic techniques are mainly divided into two major groups, i.e., identification of transgenic (1) DNA and (2) proteins from GMOs and their products. Conventional methods such as PCR (polymerase chain reaction) and enzyme-linked immunosorbent assay (ELISA) were routinely employed for DNA and protein based quantification respectively. Although, these Techniques (PCR and ELISA) are considered as significantly convenient and productive, but there is need for more advance technologies that allow for high throughput detection and the quantification of GM event as the production of more complex GMO is increasing day by day. Therefore, recent approaches like microarray, capillary gel electrophoresis, digital PCR and next generation sequencing are more promising due to their accuracy and precise detection of transgenic contents. The present article is a brief comparative study of all such detection techniques on the basis of their advent, feasibility, accuracy, and cost effectiveness. However, these emerging technologies have a lot to do with detection of a specific event, contamination of different events and determination of fusion as well as stacked gene protein are the critical issues to be addressed in future. [ABSTRACT FROM AUTHOR]

The plastic-adherent, fibroblast-like, clonogenic cells found in the human body now defined as multipotent “Mesenchymal Stromal Cells” (MSCs) hold immense potential for cell-based therapies. Recently, research and basic knowledge of these cells has fast-tracked, both from fundamental and translational perspectives. There have been important discoveries with respect to the available variety of tissue sources, the development of protocols for their easy isolation and in vitro expansion and for directed differentiation into various cell types. In addition, there has been discovery of novel abilities such as immune-modulation and further development of the use of biomaterials to aid isolation, expansion and differentiation together with improved delivery to the selected optimal tissue site. However, the molecular fingerprint of MSCs in these contexts remains imprecise and inadequate. Consequently, without this crucial knowledge it is difficult to achieve progress to determine with precision their practical developmental potentials. Detailed investigations on the global gene expression, or transcriptome, of MSCs could offer essential clues in this regard. In this article, we address the challenges associated with MSC transcriptome studies, the paradoxes observed in published experimental results and the need for careful transcriptomic analysis. We describe the exemplary applications with various transcriptome platforms that are used to address the variation in biomarkers and the identification of differentiation processes. The evolution and the potentials for adapting next-generation sequencing (NGS) technology in transcriptome analysis are discussed. Lastly, based on review of the existing understanding and published studies, we propose how NGS may be applied to promote further understanding of the biology of MSCs and their use in allied fields such as regenerative medicine. [ABSTRACT FROM AUTHOR]

Zirconium-based bulk metallic glass (Zr-based BMG) is a potential material for micro/nano molds. In this study, we investigated the flow characteristics of Zr-based BMG in the supercooled liquid region (SLR) with a series of uniaxial compression tests. The Newtonian viscosity model of Zr-based BMG is constructed by fitting with the experimental results based on the Arrhenius equation. The hot embossing process which can be seen as a combination of two kinds of simultaneous flows is theoretically analyzed based on Navier-Stokes (N-S) equations. The velocity and pressure fields in a BMG sample have been evaluated. Two kinds of filling modes are proposed considering the interfacial tension and oxide layer that forms on the BMG surface. The exact solutions are obtained when the cavities of the micro arrays are circular, triangular and rectangular in shape based on N-S equations. The theoretical solutions, considered surface resistance, are in good agreement with our experimental results. These models can be used to describe the fillings of metallic glass in micro hot embossing process. Finally, we studied the effects of micro cavity size and shape on BMG filling, and found that the cavities of micro rectangular array are easier filling in same feature size, however, the cavities of micro circular array are easier filling in same area. The exact models developed in this paper can be used to calculate the BMG filling heights in the cavities of micro arrays and verify the reliability of simulation results in future mold designs. [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.

Fibroblast growth factor 2 (FGF-2) is an abundant growth factor in the brain and exerts multiple functions on neural cells ranging from cell division, cell fate determination to differentiation. However, many details of the molecular mechanisms underlying the diverse functions of FGF-2 are poorly understood. In a comparative microarray analysis of motor sensory cortex (MSC) tissue of adult knockout (FGF-2 −/− ) and control (FGF-2 +/+ ) mice, we found a substantial number of regulated genes, which are implicated in cytoskeletal machinery dynamics. Specifically, we found a prominent downregulation of Arhgef6. Arhgef6 mRNA was significantly reduced in the FGF-2 −/− cortex, and Arhgef6 protein virtually absent, while RhoA protein levels were massively increased and Cdc42 protein levels were reduced. Since Arhgef6 is localized to dendritic spines, we next analyzed dendritic spines of adult FGF2 −/− and control mouse cortices. Spine densities were significantly increased, whereas mean length of spines on dendrites of layer V of MSC neurons in adult FGF-2 −/− mice was significantly decreased as compared to respective controls. Furthermore, neurite length in dissociated cortical cultures from E18 FGF-2 −/− mice was significantly reduced at DIV7 as compared to wildtype neurons. Despite the fact that altered neuronal morphology and alterations in dendritic spines were observed, FGF-2 −/− mice behave relatively unsuspicious in several behavioral tasks. However, FGF-2 −/− mice exhibited decreased thermal pain sensitivity in the hotplate-test. [ABSTRACT FROM AUTHOR]

Induced regulatory T cells (iTreg) are imperative for tolerance induction and spreading of infectious tolerance. Ex vivo generated tolerogenic dendritic cells (tDCs) have strong therapeutic potential to induce antigen-specific iTreg. We previously demonstrated that IL-10 tDC-primed T cells are very suppressive and produce IL-10. Here, we show that the majority of IL-10 + T cells co-express IFNγ, giving rise to the question whether these cells are proinflammatory or regulatory. Whole genome gene expression analysis revealed a strong regulatory gene profile and a suppressed Th1 gene profile for IL-10/IFNγ co-expressing CD4 + T cells. Protein analysis confirmed an extensive regulatory phenotype for IL-10 + /IFNγ + T cells, with specific enhanced expression of GARP and PD-1. In line with these data, isolated IL-10 + /IFNγ + T cells displayed potent suppressive capacity. Thus, IL-10/IFNγ co-expressing CD4 + T cells induced by IL-10 tDC show dominance of immunomodulation over Th1-mediated immunoactivation and can contribute to induction or spreading of immunological tolerance. [ABSTRACT FROM AUTHOR]

Dental implants are one of the most frequently used treatment options for tooth replacement. Approximately 30% of patients with dental implants develop peri-implantitis, which is an oral inflammatory disease that leads to loss of the supporting tissues, predominately the bone. For the development of future therapeutic strategies, it is essential to understand the molecular pathophysiology of human dental peri-implant infections. Here, we describe the gene and protein expression patterns of peri-implantitis bone tissue compared with healthy peri-implant bone tissue. Furthermore, cells from the osteoblastic lineage derived from peri-implantitis samples were immortalized and characterized. We applied microarray, quantitative reverse transcription polymerase chain reaction, fluorescence-activated cell sorting, and Western blot analyses. The levels of typical bone matrix molecules, including SPP1, BGLAP, and COL9A1, in patients with peri-implantitis were reduced, while the inflammation marker interleukin 8 (IL8) was highly expressed. RUNX2, one of the transcription factors of mature osteoblasts, was also decreased in peri-implantitis. Finally, the human telomerase reverse transcriptase immortalized cell line from peri-implantitis exhibited a more fibro-osteoblastic character than did the healthy control. [ABSTRACT FROM PUBLISHER]

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Purpose: Management of Retinoblastoma (RB), a pediatric ocular cancer is limited by drug-resistance and drug-dosage related side effects during chemotherapy. Molecular de-regulation in post-chemotherapy RB tumors was investigated. Materials and Methods: cDNA microarray analysis of two post-chemotherapy and one pre-chemotherapy RB tumor tissues was per¬formed, followed by Principle Component Analysis, Gene ontology, Pathway Enrichment analysis and Biological Analysis Network (BAN) modeling. The drug modulation role of two significantly up-regulated genes (p0.05) ― Ect2 (Epithelial-cell-transforming-sequence-2), and PRAME (preferentially-expressed-Antigen-in-Melanoma) was assessed by qRT-PCR, immunohistochemistry and cell viability assays. Results: Differential up-regulation of 1672 genes and down-regulation of 2538 genes was observed in RB tissues (relative to normal adult retina), while 1419 genes were commonly de-regulated between pre-chemotherapy and post- chemotherapy RB. Twenty one key gene ontology categories, pathways, biomarkers and phenotype groups harboring 250 differentially expressed genes were dys-regulated (EZH2, NCoR1, MYBL2, RB1, STAMN1, SYK, JAK1/2, STAT1/2, PLK2/4, BIRC5, LAMN1, Ect2, PRAME and ABCC4). Differential molecular expressions of PRAME and Ect2 in RB tumors with and without chemotherapy were analyzed. There was neither up- regula¬tion of MRP1, nor any significant shift in chemotherapeutic IC50, in PRAME over-expressed versus non-transfected RB cells. Conclusion: Cell cycle regulatory genes were dys-regulated post-chemotherapy. Ect2 gene was expressed in response to chemotherapy-induced stress. PRAME does not contribute to drug resistance in RB, yet its nuclear localization and BAN information, points to its possible regulatory role in RB. [ABSTRACT FROM AUTHOR]

Worldwide, the incidence of oral tongue cancer is on the rise, adding to the existing burden due to prevailing low survival and high recurrence rates. This study uses high-throughput expression profiling to identify candidate markers of resistance/response in patients with oral tongue cancer. Analysis of primary and post-treatment samples (12 tumor and 8 normal) by the Affymetrix platform (HG U133 plus 2) identified 119 genes as differentially regulated in recurrent tumors. The study groups had distinct profiles, with induction of immune response and apoptotic pathways in the non-recurrent and metastatic/invasiveness pathways in the recurrent group. Validation was carried out in tissues by Quantitative Real-Time PCR (QPCR) (n=30) and immunohistochemistry (IHC) (n=35) and in saliva by QPCR (n=37). The markers, COL5A1, HBB, IGLA and TSC individually and COL5A1 and HBB in combination had the best predictive power for treatment response in the patients. A subset of markers identified (COL5A1, ABCG1, MMP1, IL8, FN1) could be detected in the saliva of patients with oral cancers with their combined sensitivity and specificity being 0.65 and 0.87 respectively. The study thus emphasizes the extreme prognostic value of exploring markers of treatment resistance that are expressed in both tissue and saliva. [ABSTRACT FROM AUTHOR]

We hypothesized that circulating leukocyte RNA profiles or “riboleukograms” detect ventilator-associated pneumonia after blunt trauma.A pilot microarray study of 11 ventilator-associated pneumonia (VAP) patients suggested that 85 leukocyte genes can be used to diagnose VAP. Validation of this gene set to detect VAP was tested using data from an independent patient cohort.A total of 158 intubated blunt trauma patients were enrolled at 5 centers, where 57 (36%) developed VAP. Patient age was 34.2 ± 11.1 years; 65% were male. Circulating leukocyte GeneChip U133 2.0 expression values were measured at time 0.5, 1, 4, 7, 14, 21, and 28 days after injury. DChip normalized leukocyte transcriptional profiles were analyzed using repeated measures logistic regression. A compound covariate model based on leukocyte gene transcriptional profiles in a training subset of patients was tested to determine predictive accuracy for VAP 4 days prior to clinical diagnosis in the test subset.Using gene expression values measured on each study day at an FDR <0.05, 27 (32%) of the 85 genes were associated with the diagnosis of VAP 1 to 4 days before diagnosis. However, the compound covariate model based on these 85-genes did not predict VAP in the test cohort better than chance (P = 0.27). In contrast, a compound covariate model based upon de novo transcriptional analysis of the 158 patients predicted VAP better than chance 4 days before diagnosis with a sensitivity of 57% and a specificity of 69%.Our results validate those described in a pilot study, confirming that riboleukograms are associated with the development of VAP days prior to clinical diagnosis. Similarly, a riboleukogram predictive model tested on a larger cohort of 158 patients was better than chance at predicting VAP days prior to clinical diagnosis. [ABSTRACT FROM AUTHOR]

This paper describes a microfluidic chip in which two perpendicular laminar-flow streams can be operated to sequentially address the surface of a flow-chamber with semi-parallel sample streams. The sample streams can be controlled in position and width by the method of electrokinetic focusing. For this purpose, each of the two streams is sandwiched by two parallel sheath flow streams containing just a buffer solution. The streams are being electroosmotically pumped, allowing a simple chip design and a setup with no moving parts. Positioning of the streams was adjusted in real-time by controlling the applied voltages according to an analytical model. The perpendicular focusing gives rise to overlapping regions, which, by combinatorial (bio) chemistry, might be used for fabrication of spot arrays of immobilized proteins and other biomolecules. Since the patterning procedure is done in a closed, liquid filled flow-structure, array spots will never be exposed to air and are prevented from drying. With this device configuration, it was possible to visualize an array of 49 spots on a surface area of 1 mm2. This article describes the principle, fabrication, experimental results, analytical modeling and numerical simulations of the microfluidic chip. [ABSTRACT FROM AUTHOR]