Prevalence of anaerobic bacteria [P.gingivalis] as major microbial agent in the incidence periodontal diseases by meta-analysis

Statement of the Problem: Periodontal diseases are complex oral diseases charac-terized by bacterial-induced inflammatory destruction of tooth-supporting tissues. Porphyromonas gingivalis [P. gingivalis] is a common gram-negative anaerobic oral bacteria strongly associated with periodontal disease

Purpose: The present study was conducted to estimate prevalence of P. gingivalis in patients with periodontal diseases by using meta-analysis method

Martials and Method: Different databases including PubMed, EmBase, Scopus, the Institute for Scientific Information [ISI] Web of Science, and the Cochrane Library were searched to identify original English-language studies addressing prevalence of P. gingivalis in periodontal diseases up to December 2014. The ran-dom effects model was applied in the meta-analysis and the heterogeneity between studies was assessed using a Cochran test and the I2 index. Funnel plots and Egger test were used to examine publication bias. Statistical analyses were performed using STATA version 12

Results: Forty-two eligible studies published during 1993- 2016 were selected for meta-analysis. Considering all the included studies, the total sample size was 5,884 individuals containing 2,576 healthy people with a mean age of 37.21+/-7.45 years and 3,308 periodontal patients with a mean age of 44.16+/-8.35 years. Overall, the prevalence of P. gingivalis was 78% [95% CI: 74-81] in periodontal diseases group and 34% [95% CI: 26-41] in healthy individuals. There was a significantly higher prevalence of P.gingivalis in individuals with periodontal diseases compared to healthy subjects [78% versus 34%, respectively]

Conclusion: This study indicates that P. gingivalis is highly present in subjects with periodontal diseases and it also appears in periodontally healthy people, alt-hough to a lesser extent. Thus, the presence of P. gingivalis increases the chance of periodontal disease and it can be considered as a main potential risk factor

Celiac disease microarray analysis based on system biology approach

Aim: Aim of this study is screen of the large numbers of related genes of CD to find the key ones

Background: Celiac disease [CD] is known as a gluten sensitive and immune system dependent disease. There are several high throughput investigations about CD but it is necessary to clarify new molecular aspects mechanism of celiac

Methods: Whole-genome profile [RNA] of the human peripheral blood mononuclear cells [PBMCs] as Gene expression profile GSE113469 was retrieved Gene Expression Omnibus [GEO] database. The significant genes were selected and analyzed via proteinprotein interaction [PPI] network by Cytoscape software. The key genes were introduced and enriched via ClueGO to find the related biochemical pathways

Results: Among 250 significant genes 47 genes with expressed change above 2 fold change [FC] were interacted and the constructed network were analyzed. The network characterized by poor connections so it was promoted by addition 50 related nodes and 18 crucial nodes were introduced. Two clusters of biochemical pathways were identified and discussed

Conclusion: There is an obvious conflict between microarray finding and the well-known related genes of CD. This problem can be solve by more attention to the interpretation of PPI ntwork analysis results

Lavandula angustifolia effects on rat models of alzheimer's disease through the investigation of serum metabolic features using NMR metabolomics

Background: Alzheimer's Disease [AD] is the most prevalent cause of memory impairment in the elderly population, but the diagnosis and treatment of the disease is still challenging. Lavender aqueous extract has recently been shown to have the potential in clearing Amyloid-beta plaques from AD rat hippocampus. To elucidate the therapeutic mechanisms of lavender, serum metabolic fingerprint of Abeta-induced rat Alzheimer's models was investigated through nuclear magnetic resonance spectrometry

Methods: For the establishment of rat Alzheimer's models, 10 microg of Amyloid beta 1-42 was injected to male Wistar rats. The lavender aqueous extract was injected 20 days after the establishment of the models, once daily for 20 days. Serum samples were collected and metabolite fingerprints were obtained using 500 MHz 1H-NMR spectrometry, following multivariate statistical analyses. The resulted metabolites were then subjected to pathway analysis tools to reveal metabolic pathways affected by the lavender extract treatment

Results: Levels of 10 metabolite markers including alanine, glutamine, serine, isoleucine, valine, carnitine, isobutyrate, pantothenate, glucose and asparagine were reversed nearly to control values after treatment with lavender extract. The results revealed that the most significantly affected pathways during treatment with lavender extract belonged to carbohydrate and amino acid metabolism, including pantothenate and CoA metabolism, glyoxilate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, cysteine and methionine metabolism

Conclusion: As lavender extract reversed the direction of changes of some metabolites involved in AD pathogenesis, it was concluded that the extract might play a role in the disease improvement and serve as a potential therapeutic option for the treatment of AD. Moreover, the metabolites which were found in AD rats could serve as a potential marker panel for the disease; however, much further investigation and validation of the results is needed

Biochemical pathway analysis of gastric atrophy

Aim: Pathway analysis of gastric atrophy to find new molecular prospective of disease

Background: Gastric atrophy as a process which is accompanied with "loss of glans" in stomach can be considered as a risk factor of gastric cancer. Here, the correlated biochemical pathways to the disorder have been analyzed via protein-protein interaction [PPI] network analysis

Methods: The genes related to gastric atrophy were retrieved by STRING database and organized in a network by Cytoscape. Three significant clusters were determined by Cluster ONE plug-in of Cytoscape. The elements of cluster-2 which contained all central nodes of the network were enriched by ClueGO and the biochemical pathways discussed in details

Results: The number of seven central nodes [which are included in cluster-2]; INS, TP53, IL6, TNF, SRC, MYC, and IL8 were identified. The biochemical pathways related to the elements of cluster-2 were determined and clustered in nine groups. The pathways were discussed in details

Conclusion: Pathway analysis indicates that the introduced central genes of the network can be considered as biomarkers of gastric atrophy

Celiac disease and hepatitis C relationships in transcriptional regulatory networks

Aim: we mainly aimed to elucidate potential comorbidities between celiac disease and hepatitis c by means of data and network analysis approaches

Background: understanding the association among the disorders evidently has important impact on the diagnosis and therapeutic approaches. Celiac disease is the most challenging, common types of autoimmune disorders. On the other hand, hepatitis c virus genome products like some proteins are supposed to be resemble to gliadin types that in turn activates gluten intolerance in people with inclined to gluten susceptibilities. Moreover, a firm support of association between chronic hepatitis and celiac disease remains largely unclear. Henceforth exploring cross-talk among these diseases will apparently lead to the promising discoveries concerning important genes and regulators

Methods: 321 and 1032 genes associated with celiac disease and hepatitis c retrieved from DisGeNET were subjected to build a gene regulatory network. Afterward a network-driven integrative analysis was performed to exploring prognosticates genes and related pathways

Results: 105 common genes between these diseases included 11 transcription factors were identified as hallmark molecules where by further screening enriched in biological GO terms and pathways chiefly in immune systems and signaling pathways such as chemokines, cytokines and interleukins

Conclusion: in silico data analysis approaches indicated that the identified selected combinations of genes covered a wide range of known functions triggering the inflammation implicated in these diseases

Structural biology: modeling applications and techniques at a glance

As recent advancements in biology shows, the molecular machines specially proteins, RNA and complex molecules play the main role of the so called cell functionality. It means a very big part of the system biology is concerned with the interactions of such molecular components. Drug industries and research institutes are trying hard to better understand the concepts underlying these interactions and are highly dependent on the issues regarding these molecular elements. However the costs for such projects are so high and in many cases these projects will be funded by governments or profit making companies. With this in mind it has to be said that the techniques like stimulation are always a very good candidate to decrease such costs and to provide scientists with a bright future of the project results before undergoing costly experiments. However the costs involved projects that determine an approximation for the problem is not that much high but they are also costly. So it is of utmost importance to invent special techniques for the concept of stimulation that can also decrease the project costs and also predict much accurately. Since the system biology and proteomics as the study of the proteins and their functions are in the center of consideration for the purpose of drug discovery, understanding the cell functionalities and the underlying causes behind diseases; so we need advance software and algorithms that can predict the structure of the molecular components and to provide researchers with the computational tools to analyze such models. In this paper we make review of the importance of molecular modeling, its limitations and applications

Common proteomic technologies, applications, and their limitations

Proteomics refers to the analysis of expression, localization, functions, posttranslational modifications, and interactions of proteins expressed by a genome at a specific condition and at a specific time. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. In this review, we have focused on the proteomics methods: gel-based and gel-free techniques and discussed their applications and challenges in the field of proteomics

Effect of hyperglycemic condition on observed surface area to volume ratio of human cancerous cells

The human blood basal glucose level is a completely controlled range. Information on the relationship between culture glucose concentration and changes in the cell's surface area, volume and surface area to volume [S/V] ratio are lacking. Cancerous epithelial-like cell lines SW480, SW742 and T-47D were cultured in mediums nourished with 4.7 mM per liter of glucose as the control group and three other groups with glucose concentrations of 9.4, 14.1 and 18.8 mM at 37[degree]C for 48hr. Digital images of cells were analyzed using the Image J software. Observed changes in surface area, volume, and surface area to volume [S/V] ratio were significantly [P<0.05] different between the control group and the X4 group [18.8 mM glucose] in the three cell lines tested. Cultured cells responded delicately but sharply to glucose elevation. The goal of this research is to show the dictating of changes via pathologic conditions in cellular levels that could be a good answer to changing the body metabolic parameters. Besides the S/V ratio could be studied as a variant parameter in other metabolic challenges

Gene sets involved in prostate cancer based on differential expression

Prostate cancer is the second most common cancer in men. In spite of on-going researches in this filed, the specific causes of prostate cancer are so far unknown. In this study, we used two methods of Gene Set Analysis to improve the biological interpretation of the observed expression patterns in prostate cancer. The Gene Set Analysis is a computational method to discover gene sets whose expression is associated with a phenotype of interest. In addition, we used these methods to search gene sets defined by KEGG and BioCarta. Although, our results showed that most of the gene sets were associated with prostate cancer in the Category and Hotelling's T[2] methods, the power of the Hotelling's T[2] was more than Category method in either KEGG or BioCarta gene sets. The concordance between the results of Pubmed articles and KEGG gene sets was more than the results of Pubmed articles and BioCarta gene sets

Appraisal of fibroblast viability in different concentration of glucose as mimicry diabetic condition

Diabetes mellitus is a common name for a group of diseases of a much diversified etiopathogenesis, characterized by chronically increased concentration of blood glucose. Diabetes results from alterations of various genes, each having a partial and additive effect. The inheritance pattern is thus complex, and environmental factors play an important role in favoring or delaying the expression of the disease. Diabetes can cause devastating complications, including cardiovascular diseases, kidney failure, and blindness, leg and foot amputations, delay in wound healing, which often result in disability and death. Fibroblast cells play a critical role in wound healing. They are the most common cells of connective tissue in animals. Tissue damage stimulates fibrocystic and induces the mitosis of fibroblasts. Wound healing processes in diabetic patients are so longer and sometimes cause to cut damaged tissue. In this study Fibroblasts were isolated from foreskin and cultured as primary cell culture in different concentrations of glucose [8.8 mmol/l, 13.13 mmol/l, 18.31 mmol/l, 27.7 mmol/l, 37.18 mmol/l, 47.17 mmol/l, 83.24 mmol/l, 124.8 mmol/l and 166.4 mmol/l] for 48h incubation time. Traditionally, the determination of cell growth is done by counting viable cells after staining with a vital dye. Among several approaches have been used in the past, The MTT method of cell determination is most invaluable to cultures which are prepared in multiwall plates. This assay is a sensitive, quantitative and reliable colorimetric assay that measures viability, proliferation and activation of cells. The results of this preliminary study suggest that altered glucose concentrations may affect fibroblast behavior in ways that are important for tissue repair and wound healing. The cells had low level of confluency and viability and in high concentration fibroblast had very low cell division