Lung granuloma: A clinicopathologic study of 158 cases

Background and Aims: A granuloma is a common pathological diagnosis in lung biopsies and is caused by a variety of etiologies. The aim of this study was to assess the etiology and frequency of different cases of lung granulomas. Methods: The medical records of all patients who had lung granulomas between 2005 and 2013 were retrospectively reviewed. Based on the histological features of the granulomas, along with the clinical, laboratory, and radiological findings, an attempt was made to identify the etiology of the granuloma in each case. Results: A total of 158 patients with lung biopsy specimens showing lung granulomas were identified. The histological findings revealed necrotizing granulomas in 92 (58%) of the cases and nonnecrotizing granulomas in 66 (42%). A definite etiology was determined in 133 cases (84%), whereas in 26 cases (16%), the etiology could not be identified despite an extensive workup. Infection was the most frequent cause of granuloma, accounting for 105 cases (66%). Mycobacterial tuberculosis (TB) was the type of infection that caused the largest number of granulomas, and was responsible for 100 cases (63%). Among the noninfectious etiologies of lung granuloma, sarcoidosis was the most common cause, accounting for 20 (13%) of the cases. Conclusions: Mycobacterial TB and sarcoidosis are the most common causes of lung granulomas in our region. In a substantial proportion of cases, the cause may not be identified despite an extensive workup

Automatic Partitioning of Large Scale Simulation in Grid Computing for Run Time Reduction

Simulating large-scale systems usually entails exhaustive computational powers and lengthy execution times. The goal of this research is to reduce execution time of large-scale simulations without sacrificing their accuracy by partitioning a monolithic model into multiple pieces automatically and executing them in a distributed computing environment. While this partitioning allows us to distribute required computational power to multiple computers, it creates a new challenge of synchronizing the partitioned models. In this article, a partitioning methodology based on a modified Prim’s algorithm is proposed to minimize the overall simulation execution time considering 1) internal computation in each of the partitioned models and 2) time synchronization between them. In addition, the authors seek to find the most advantageous number of partitioned models from the monolithic model by evaluating the tradeoff between reduced computations vs. increased time synchronization requirements. In this article, epoch- based synchronization is employed to synchronize logical times of the partitioned simulations, where an appropriate time interval is determined based on the off-line simulation analyses. A computational grid framework is employed for execution of the simulations partitioned by the proposed methodology. The experimental results reveal that the proposed approach reduces simulation execution time significantly while maintaining the accuracy as compared with the monolithic simulation execution approach

Integrated Exon Level Expression Analysis of Driver Genes Explain Their Role in Colorectal Cancer

<div><p>Integrated analysis of genomic and transcriptomic level changes holds promise for a better understanding of colorectal cancer (CRC) biology. There is a pertinent need to explain the functional effect of genome level changes by integrating the information at the transcript level. Using high resolution cytogenetics array, we had earlier identified driver genes by ‘Genomic Identification of Significant Targets In Cancer (GISTIC)’ analysis of paired tumour-normal samples from colorectal cancer patients. In this study, we analyze these driver genes at three levels using exon array data – gene, exon and network. Gene level analysis revealed a small subset to experience differential expression. These results were reinforced by carrying out separate differential expression analyses (SAM and LIMMA). ATP8B1 was found to be the novel gene associated with CRC that shows changes at cytogenetic, gene and exon levels. Splice index of 29 exons corresponding to 13 genes was found to be significantly altered in tumour samples. Driver genes were used to construct regulatory networks for tumour and normal groups. There were rearrangements in transcription factor genes suggesting the presence of regulatory switching. The regulatory pattern of AHR gene was found to have the most significant alteration. Our results integrate data with focus on driver genes resulting in highly enriched novel molecules that need further studies to establish their role in CRC.</p></div

Core Analysis of Differentially Expressed Genes using IPA.

<p>Core analysis using IPA was carried out using set of 760 genes that were differentially expressed in tumour samples. Important biological functions (a) pathways (b) and networks (c-e) were revealed by this analysis.</p

Differentially regulated genes found to have incoherent expression levels and genomic changes.

<p>AA  =  Fold change value as calculated by AltAnalyze program.</p><p>EC  =  Fold change value as calculated by Expression Console program.</p><p>TF  =  Transcription Factor. Unknown is the TF that is not found in the driver genes.</p><p>Differentially regulated genes found to have incoherent expression levels and genomic changes.</p

Differential Expression at exon level was observed in thirteen GISTIC genes.

<p>Exon expression (i) and splice index (ii) values were mapped for both tumour and normal samples for twenty nine exons affecting thirteen genes. Exon 25-1 of MUC4 gene (a) shows highest negative splice index value (a ii) whereas exon 2-2 of IL6 (b) showed highest value of 2.44 (b ii). Exon expression and splice index values for rest 27 exons are provided as supplementary <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110134#pone-0110134-g001" target="_blank">figure 1</a>.</p

Principal Component Analysis of Exon array data from 32 patients.

<p>60 samples from 32 patients were subjected to PCA and the outliers were removed. 4 normal and 7 tumour samples were removed from the final analysis.</p

Significant change in expression value at gene level was observed in 20/144 genes.

<p>Two different algorithms were used to measure expression values from Exon array data to support the results. AltAnalyze (1a) and Expression Console (1b) show complimentary results with maximum changes observed in BCAS1, INHBA, IL6 and MUC4 genes.</p