wpLogicNet: logic gate and structure inference in gene regulatory networks.

MOTIVATION: The gene regulatory process resembles a logic system in which a target gene is regulated by a logic gate among its regulators. While various computational techniques are developed for a gene regulatory network (GRN) reconstruction, the study of logical relationships has received little attention. Here, we propose a novel tool called wpLogicNet that simultaneously infers both the directed GRN structures and logic gates among genes or transcription factors (TFs) that regulate their target genes, based on continuous steady-state gene expressions. RESULTS: wpLogicNet proposes a framework to infer the logic gates among any number of regulators, with a low time-complexity. This distinguishes wpLogicNet from the existing logic-based models that are limited to inferring the gate between two genes or TFs. Our method applies a Bayesian mixture model to estimate the likelihood of the target gene profile and to infer the logic gate a posteriori. Furthermore, in structure-aware mode, wpLogicNet reconstructs the logic gates in TF-gene or gene-gene interaction networks with known structures. The predicted logic gates are validated on simulated datasets of TF-gene interaction networks from Escherichia coli. For the directed-edge inference, the method is validated on datasets from E.coli and DREAM project. The results show that compared to other well-known methods, wpLogicNet is more precise in reconstructing the network and logical relationships among genes. AVAILABILITY AND IMPLEMENTATION: The datasets and R package of wpLogicNet are available in the github repository, https://github.com/CompBioIPM/wpLogicNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hepatogenesis and hepatocarcinogenesis: Alignment of the main signaling pathways.

Development is a symphony of cells differentiation in which different signaling pathways are orchestrated at specific times and periods to form mature and functional cells from undifferentiated cells. The similarity of the gene expression profile in malignant and undifferentiated cells is an interesting topic that has been proposed for many years and gave rise to the differentiation-therapy concept, which appears a rational insight and should be reconsidered. Hepatocellular carcinoma (HCC), as the sixth common cancer and the third leading cause of cancer death worldwide, is one of the health-threatening complications in communities where hepatotropic viruses are endemic. Sedentary lifestyle and high intake of calories are other risk factors. HCC is a complex condition in which various dimensions must be addressed, including heterogeneity of cells in the tumor mass, high invasiveness, and underlying diseases that limit the treatment options. Under these restrictions, recognizing, and targeting common signaling pathways during liver development and HCC could expedite to a rational therapeutic approach, reprograming malignant cells to well-differentiated ones in a functional state. Accordingly, in this review, we highlighted the commonalities of signaling pathways in hepatogenesis and hepatocarcinogenesis, and comprised an update on the current status of targeting these pathways in laboratory studies and clinical trials.

Competitive exclusion during co-infection as a strategy to prevent the spread of a virus: A computational perspective.

Inspired by the competition exclusion principle, this work aims at providing a computational framework to explore the theoretical feasibility of viral co-infection as a possible strategy to reduce the spread of a fatal strain in a population. We propose a stochastic-based model-called Co-Wish-to understand how competition between two viruses over a shared niche can affect the spread of each virus in infected tissue. To demonstrate the co-infection of two viruses, we first simulate the characteristics of two virus growth processes separately. Then, we examine their interactions until one can dominate the other. We use Co-Wish to explore how the model varies as the parameters of each virus growth process change when two viruses infect the host simultaneously. We will also investigate the effect of the delayed initiation of each infection. Moreover, Co-Wish not only examines the co-infection at the cell level but also includes the innate immune response during viral infection. The results highlight that the waiting times in the five stages of the viral infection of a cell in the model-namely attachment, penetration, eclipse, replication, and release-play an essential role in the competition between the two viruses. While it could prove challenging to fully understand the therapeutic potentials of viral co-infection, we discuss that our theoretical framework hints at an intriguing research direction in applying co-infection dynamics in controlling any viral outbreak's speed.

Evaluation of Toxoplasma gondii infection in western Iran: seroepidemiology and risk factors analysis.

BACKGROUND: Toxoplasmosis is a parasitic infectious disease, and Toxoplasma gondii is the causative factor of this intracellular protozoan disease. Due to the lack of information about the rate of T. gondii in general papulation of Markazi Province in Iran, the current study was conducted to determine the prevalence of toxoplasmosis and the related risk factor analysis in the general population of Markazi Province. METHODS: This cross-sectional study was performed within 6 months on individuals who were referred to diagnostic laboratories in Markazi Province. The demographic and background information of the subjects were collected using a questionnaire. Three milliliters of blood samples was collected from the participants under sterile conditions. The sera were separated and evaluated for levels of anti-Toxoplasma IgG antibody using a commercial enzyme-linked immunosorbent assay (ELISA) method. The collected data were analyzed by the SPSS software using descriptive statistics and chi-square test. RESULTS: Out of 824 people from the general population of Markazi Province who were investigated in this study, 276 (33.5%) had anti-Toxoplasma antibodies in their blood. According to the logistic regression model, gender variables, location, marital status, and having a cat at home do not affect the chances of contracting the parasite. Furthermore, the chance of contracting the parasite in 41- to 50-year-olds is 0.85 times the one in the 20- to 30-year-olds. The prevalence of toxoplasmosis in men and women in Markazi Province was 33% and 34.5%, respectively. CONCLUSION: The mean prevalence of T. gondii infection in the age groups of 20-40, and ≥ 40 years was estimated to be 24.7%, and 40.8%, respectively. These rates were significantly lower than the national results (44%, and 55%, respectively). Therefore, regarding to the health authorities, it is necessary to raise the level of awareness of people of the region, especially at-risk groups about the transmittance and prevention methods, and infection risk factors in order to prevent the occurrence of T. gondii infection and reduce the prevalence and incidence of the disease.

A Network-Based Comparison Between Molecular Apocrine Breast Cancer Tumor and Basal and Luminal Tumors by Joint Graphical Lasso.

Joint graphical lasso (JGL) approach is a Gaussian graphical model to estimate multiple graphical models corresponding to distinct but related groups. Molecular apocrine (MA) breast cancer tumor has similar characteristics to luminal and basal subtypes. Due to the relationship between MA tumor and two other subtypes, this paper investigates the similarities and differences between the MA genes association network and the ones corresponding to other tumors by taking advantageous of JGL properties. Two distinct JGL graphical models are applied to two sub-datasets including the gene expression information of the MA and the luminal tumors and also the MA and the basal tumors. Then, topological comparisons between the networks such as finding the shared edges are applied. In addition, several support vector machine (SVM) classification models are performed to assess the discriminating power of some critical nodes in the networks, like hub nodes, to discriminate the tumors sample. Applying the JGL approach prepares an appropriate tool to observe the networks of the MA tumor and other subtypes in one map. The results obtained by comparing the networks could be helpful to generate new insight about MA tumor for future studies.

The effects of applying an assessment form based on the health functional patterns on nursing student's attitude and skills in developing the nursing process.

OBJECTIVES: Comprehensive nursing assessment, as the first step in the nursing process, involves the systematic and constant data gathering to facilitate the development of the patient-specific nursing process. The aim of this study is to determine the effects of applying an assessment form based on the health functional patterns on nursing student's attitude and skills in developing nursing process. METHODS: A randomized controlling design was conducted. Of 84 undergraduate nursing students, 42 students were allocated to the intervention or control group. In clinical education, a patient assessment form based on Gordon's functional health patterns was applied to help students in the intervention group to develop nursing process, while the control group received traditional methods. The data were gathered using a demographic information questionnaire, skills in nursing process development checklist, and attitudes towards nursing process questionnaire. RESULTS: The average scores for students' attitude and skills in developing nursing process in the intervention group were greater than those of the control group. CONCLUSION: Applying nursing assessment using the patient assessment form based on Gordon's functional health patterns can improve the students' learning in developing nursing process.

A Pilot Study to Evaluate the Effects of Oral N-Acetyl Cysteine on Inflammatory and Oxidative Stress Biomarkers in Rheumatoid Arthritis.

BACKGROUND: Rheumatoid Arthritis (RA) is a common inflammatory disease of the joints. Due to the importance of inflammation and oxidative stress in the pathogenesis of RA, drugs that have anti-oxidant and anti-inflammatory properties, such as N-acetyl Cysteine (NAC), can be used as adjunctive therapy in patients with RA. AIMS: The aim of this study was to evaluate the effects of oral NAC on inflammatory cytokines and oxidative stress in patients with RA. METHODS: Adjunct to standard treatment, the NAC group (23 patients) received 600 mg of NAC twice daily and the placebo group (19 patients) received identical placebo twice daily for 12 weeks. Serum levels of Total Oxidant Status (TOS), Total Antioxidant Capacity (TAC), nitric oxide (NO), Total Thiol Groups (TTG), Malondialdehyde (MDA), tumor necrosis factor-alpha (TNF-α), interleukin- 6 (IL-6), C-reactive Protein (CRP), and Erythrocyte Sedimentation Rate (ESR) were measured at baseline and at the end of the study. RESULTS: Results showed that in the NAC group, the serum levels of MDA, NO, IL-6, TNF-α, ESR and CRP were significantly lower than the baseline. Also, the serum level of TAC and TTG, as antioxidant parameters, increased significantly. However, only NO, MDA and TTG showed a significant difference in the NAC group as compared to the placebo group at the end of study. CONCLUSION: According to the results of this study, oral NAC can significantly reduce the several oxidative stress factors and inflammatory cytokines. These results need to be confirmed in larger studies while considering clinical outcomes of RA patients.

Evaluating the Effect of Oral N-acetylcysteine as an Adjuvant Treatment on Clinical Outcomes of Patients with Rheumatoid Arthritis: A Randomized, Double Blind Clinical Trial.

OBJECTIVE: Oxidative stress and Overproduction of pro-inflammatory cytokines are contributed in Rheumatoid Arthritis (RA) pathogenesis. N-acetylcysteine (NAC) is an antioxidant and antiinflammatory agent which demonstrated analgesic effects in some studies. This study is designed to assess the effects of oral NAC as an adjuvant therapy on the clinical outcomes of patients with active RA. METHODS: In this randomized clinical trial, 51 RA patients with active RA were studied in 2 groups: NAC group (27 patients) received standard treatment of RA and 600 mg NAC twice a day for 12 weeks, and placebo group (24 patients) received the standard treatment of RA and placebo. Disease activity score (DAS28) was used to assess the activity of RA, Visual Analog Scale (VAS) for the severity of pain, Health Assessment Questionnaire (HAQ) for the patients' physical performance, and Global Health (GH) parameter for the patients' assessment of their disease activity. The number of tender and swollen joints and Erythrocyte Sedimentation Rate (ESR) were also determined for each patient. Data were analyzed using SPSS version 16.0 (Chicago, IL, USA). RESULTS: After 12 weeks of intervention, there were no significant differences between two groups in DAS28 score and ESR (P values were 0.4 and 0.6, respectively). However, GH, VAS, and HAQ scores were improved significantly in the NAC group compared to the placebo group. CONCLUSION: Our findings indicate that oral administration of NAC may be associated with improving health status in RA patients and considered as an adjuvant therapy in these patients. Further studies with larger sample size, longer study duration and higher doses of NAC are needed to confirm the effects of oral NAC in RA patients.

F-MAP: A Bayesian approach to infer the gene regulatory network using external hints.

The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches.

Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods.

BACKGROUND: Hepatitis B (HB) is a major global mortality. Accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. This paper aimed to apply three different to predict monthly incidence rates of HB. METHODS: This historical cohort study was conducted on the HB incidence data of Hamadan Province, the west of Iran, from 2004 to 2012. Weighted Markov Chain (WMC) method based on Markov chain theory and two time series models including Holt Exponential Smoothing (HES) and SARIMA were applied on the data. The results of different applied methods were compared to correct percentages of predicted incidence rates. RESULTS: The monthly incidence rates were clustered into two clusters as state of Markov chain. The correct predicted percentage of the first and second clusters for WMC, HES and SARIMA methods was (100, 0), (84, 67) and (79, 47) respectively. CONCLUSIONS: The overall incidence rate of HBV is estimated to decrease over time. The comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the HES had the most accurate prediction of the incidence rates.

Finding genes discriminating smokers from non-smokers by applying a growing self-organizing clustering method to large airway epithelium cell microarray data.

BACKGROUND: Cigarette smoking is the major risk factor for development of lung cancer. Identification of effects of tobacco on airway gene expression may provide insight into the causes. This research aimed to compare gene expression of large airway epithelium cells in normal smokers (n=13) and non-smokers (n=9) in order to find genes which discriminate the two groups and assess cigarette smoking effects on large airway epithelium cells. MATERIALS AND METHODS: Genes discriminating smokers from non-smokers were identified by applying a neural network clustering method, growing self-organizing maps (GSOM), to microarray data according to class discrimination scores. An index was computed based on differentiation between each mean of gene expression in the two groups. This clustering approach provided the possibility of comparing thousands of genes simultaneously. RESULTS: The applied approach compared the mean of 7,129 genes in smokers and non-smokers simultaneously and classified the genes of large airway epithelium cells which had differently expressed in smokers comparing with non-smokers. Seven genes were identified which had the highest different expression in smokers compared with the non-smokers group: NQO1, H19, ALDH3A1, AKR1C1, ABHD2, GPX2 and ADH7. Most (NQO1, ALDH3A1, AKR1C1, H19 and GPX2) are known to be clinically notable in lung cancer studies. Furthermore, statistical discriminate analysis showed that these genes could classify samples in smokers and non-smokers correctly with 100% accuracy. With the performed GSOM map, other nodes with high average discriminate scores included genes with alterations strongly related to the lung cancer such as AKR1C3, CYP1B1, UCHL1 and AKR1B10. CONCLUSIONS: This clustering by comparing expression of thousands of genes at the same time revealed alteration in normal smokers. Most of the identified genes were strongly relevant to lung cancer in the existing literature. The genes may be utilized to identify smokers with increased risk for lung cancer. A large sample study is now recommended to determine relations between the genes ABHD2 and ADH7 and smoking.