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2.  Ibuprofen Protects Ventilator-Induced Lung Injury by Downregulating Rho-Kinase Activity in Rats 
BioMed Research International  2014;2014:749097.
Background. Ventilator-induced lung injury-(VILI-) induced endothelial permeability is regulated through the Rho-dependent signaling pathway. Ibuprofen inhibits Rho activation in animal models of spinal-cord injury and Alzheimer's disease. The study aims to investigate ibuprofen effects on high tidal volume associated VILI. Methods. Twenty-eight adult male Sprague-Dawley rats were randomized to receive a ventilation strategy with three different interventions for 2 h: (1) a high-volume zero-positive end-expiratory pressure (PEEP) (HVZP) group; (2) an HVZP + ibuprofen 15 mg/kg group; and (3) an HVZP + ibuprofen 30 mg/kg group. A fourth group without ventilation served as the control group. Rho-kinase activity was determined by ratio of phosphorylated ezrin, radixin, and moesin (p-ERM), substrates of Rho-kinase, to total ERM. VILI was characterized by increased pulmonary protein leak, wet-to-dry weight ratio, cytokines level, and Rho guanine nucleotide exchange factor (GEF-H1), RhoA activity, p-ERM/total ERM, and p-myosin light chain (MLC) protein expression. Results. Ibuprofen pretreatment significantly reduced the HVZP ventilation-induced increase in pulmonary protein leak, wet-to-dry weight ratio, bronchoalveolar lavage fluid interleukin-6 and RANTES levels, and lung GEF-H1, RhoA activity, p-ERM/total ERM, and p-MLC protein expression. Conclusion. Ibuprofen attenuated high tidal volume induced pulmonary endothelial hyperpermeability. This protective effect was associated with a reduced Rho-kinase activity.
doi:10.1155/2014/749097
PMCID: PMC4075182  PMID: 25019086
3.  Maternal Baicalin Treatment Increases Fetal Lung Surfactant Phospholipids in Rats 
Baicalin is a flavonoid compound purified from the medicinal plant Scutellaria baicalensis Georgi and has been reported to stimulate surfactant protein (SP)-A gene expression in human lung epithelial cell lines (H441). The aims of this study were to determine whether maternal baicalin treatment could increase lung surfactant production and induce lung maturation in fetal rats. This study was performed with timed pregnant Sprague-Dawley rats. One-day baicalin group mothers were injected intraperitoneally with baicalin (5 mg/kg/day) on Day 18 of gestation. Two-day baicalin group mothers were injected intraperitoneally with baicalin (5 mg/kg/day) on Days 17 and 18 of gestation. Control group mothers were injected with vehicle alone on Day 18 of gestation. On Day 19 of gestation, fetuses were delivered by cesarean section. Maternal treatment with 2-day baicalin significantly increased saturated phospholipid when compared with control group and total phospholipid in fetal lung tissue when compared with control and 1-day baicalin groups. Antenatal treatment with 2-day baicalin significantly increased maternal growth hormone when compared with control group. Fetal lung SP-A mRNA expression and maternal serum corticosterone levels were comparable among the three experimental groups. Maternal baicalin treatment increases pulmonary surfactant phospholipids of fetal rat lungs and the improvement was associated with increased maternal serum growth hormone. These results suggest that antenatal baicalin treatment might accelerate fetal rat lung maturation.
doi:10.1093/ecam/nep073
PMCID: PMC3135634  PMID: 19584080
4.  Cytotoxic Effect of Recombinant Mycobacterium tuberculosis CFP-10/ESAT-6 Protein on the Crucial Pathways of WI-38 Cells 
To unravel the cytotoxic effect of the recombinant CFP-10/ESAT-6 protein (rCFES) on WI-38 cells, an integrative analysis approach, combining time-course microarray data and annotated pathway databases, was proposed with the emphasis on identifying the potentially crucial pathways. The potentially crucial pathways were selected based on a composite criterion characterizing the average significance and topological properties of important genes. The analysis results suggested that the regulatory effect of rCFES was at least involved in cell proliferation, cell motility, cell survival, and metabolisms of WI-38 cells. The survivability of WI-38 cells, in particular, was significantly decreased to 62% with 12.5 μM rCFES. Furthermore, the focal adhesion pathway was identified as the potentially most-crucial pathway and 58 of 65 important genes in this pathway were downregulated by rCFES treatment. Using qRT-PCR, we have confirmed the changes in the expression levels of LAMA4, PIK3R3, BIRC3, and NFKBIA, suggesting that these proteins may play an essential role in the cytotoxic process in the rCFES-treated WI-38 cells.
doi:10.1155/2009/917084
PMCID: PMC2702506  PMID: 19584916
5.  Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry Can Accurately Differentiate between Mycobacterium masilliense (M. abscessus subspecies bolletti) and M. abscessus (Sensu Stricto) 
Journal of Clinical Microbiology  2013;51(9):3113-3116.
Among 36 Mycobacterium masilliense and 22 M. abscessus isolates identified by erm(41) PCR and sequencing analysis of rpoB and 23S rRNA genes, the rate of accurate differentiation between these two subspecies was 100% by cluster analysis of spectra generated by Bruker Biotyper matrix-assisted laser desorption ionization–time of flight mass spectrometry.
doi:10.1128/JCM.01239-13
PMCID: PMC3754645  PMID: 23824775
6.  Comparison of the Accuracy of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry with That of Other Commercial Identification Systems for Identifying Staphylococcus saprophyticus in Urine 
Journal of Clinical Microbiology  2013;51(5):1563-1566.
Among 30 urinary isolates of Staphylococcus saprophyticus identified by sequencing methods, the rate of accurate identification was 100% for Bruker Biotyper matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS), 86.7% for the Phoenix PID and Vitek 2 GP systems, 93.3% for the MicroScan GP33 system, and 46.7% for the BBL CHROMagar Orientation system.
doi:10.1128/JCM.00261-13
PMCID: PMC3647924  PMID: 23390286
7.  idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach 
Nucleic Acids Research  2012;40(Web Server issue):W393-W399.
Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-and-conquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at http://idtarget.rcas.sinica.edu.tw.
doi:10.1093/nar/gks496
PMCID: PMC3394295  PMID: 22649057
8.  Inferring Genetic Interactions via a Data-Driven Second Order Model 
Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.
doi:10.3389/fgene.2012.00071
PMCID: PMC3342528  PMID: 22563331
gene expression; genetic interaction; microarray data; pathway; regression; transcriptional regulatory interaction
9.  Patient-oriented simulation based on Monte Carlo algorithm by using MRI data 
Background
Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation.
Methods
In this study, an individualized brain is modeled based on in vivo MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system.
Results
Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement.
Conclusions
In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.
doi:10.1186/1475-925X-11-21
PMCID: PMC3355000  PMID: 22510474
Patient-oriented simulation; Time-resolved Monte Carlo; Brain modeling; Spatial sensitivity profile
10.  Postnatal Corticosteroids for Prevention and Treatment of Chronic Lung Disease in the Preterm Newborn 
Despite significant progress in the treatment of preterm neonates, bronchopulmonary dysplasia (BPD) continues to be a major cause of neonatal morbidity. Affected infants suffered from long-term pulmonary and nonpulmonary sequel. The pulmonary sequels include reactive airway disease and asthma during childhood and adolescence. Nonpulmonary sequels include poor coordination and muscle tone, difficulty in walking, vision and hearing problems, delayed cognitive development, and poor academic achievement. As inflammation seems to be a primary mediator of injury in pathogenesis of BPD, role of steroids as antiinflammatory agent has been extensively studied and proven to be efficacious in management. However, evidence is insufficient to make a recommendation regarding other glucocorticoid doses and preparations. Numerous studies have been performed to investigate the effects of steroid. The purpose of this paper is to evaluate these studies in order to elucidate the beneficial and harmful effects of steroid on the prevention and treatment of BPD.
doi:10.1155/2012/315642
PMCID: PMC3189570  PMID: 22007245
11.  Inferring genetic interactions via a nonlinear model and an optimization algorithm 
BMC Systems Biology  2010;4:16.
Background
Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.
Results
An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.
Conclusions
GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.
doi:10.1186/1752-0509-4-16
PMCID: PMC2848194  PMID: 20184777
12.  Uncovering transcriptional interactions via an adaptive fuzzy logic approach 
BMC Bioinformatics  2009;10:400.
Background
To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.
Results
AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms.
Conclusion
AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.
doi:10.1186/1471-2105-10-400
PMCID: PMC2797023  PMID: 19961622
13.  Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling 
BMC Bioinformatics  2008;9:134.
Background
With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.
Results
Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.
Conclusion
SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.
doi:10.1186/1471-2105-9-134
PMCID: PMC2323972  PMID: 18312694

Results 1-13 (13)