Location of Pathogenic Bacteria during Persistent Infections: Insights from an Analysis Using Game Theory

Bacterial persistent infections are responsible for a significant amount of the human morbidity and mortality. Unlike acute bacterial infections, it is very difficult to treat persistent bacterial infections (e.g. tuberculosis). Knowledge about the location of pathogenic bacteria during persistent infection will help to treat such conditions by designing novel drugs which can reach such locations. In this study, events of bacterial persistent infections were analyzed using game theory. A game was defined where the pathogen and the host are the two players with a conflict of interest. Criteria for the establishment of Nash equilibrium were calculated for this game. This theoretical model, which is very simple and heuristic, predicts that during persistent infections pathogenic bacteria stay in both intracellular and extracellular compartments of the host. The result of this study implies that a bacterium should be able to survive in both intracellular and extracellular compartments of the host in order to cause persistent infections. This explains why persistent infections are more often caused by intracellular pathogens like Mycobacterium and Salmonella. Moreover, this prediction is in consistence with the results of previous experimental studies.

Streptococcus pneumoniae Genome Database (SPGDB): A database for strain specific comparative analysis of Streptococcus pneumoniae genes and proteins

Streptococcus pneumoniae causes pneumonia, septicemia and meningitis. S. pneumoniae is responsible for significant mortality both in children and in the elderly. In recent years, the whole genome sequencing of various S. pneumoniae strains have increased manifold and there is an urgent need to provide organism specific annotations to the scientific community. This prompted us to develop the Streptococcus pneumoniae Genome Database (SPGDB) to integrate and analyze the completely sequenced and available S. pneumoniae genome sequences. Further, links to several tools are provided to compare the pool of gene and protein sequences, and proteins structure across different strains of S. pneumoniae. SPGDB aids in the analysis of phenotypic variations as well as to perform extensive genomics and evolutionary studies with reference to S. pneumoniae. (C) 2014 Elsevier Inc. All rights reserved.

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.