A model-based analysis identifies differences in phenotypic resistance between in vitro and in vivo: implications for translational medicine within tuberculosis.

Proper characterization of drug effects on Mycobacterium tuberculosis relies on the characterization of phenotypically resistant bacteria to correctly establish exposure-response relationships. The aim of this work was to evaluate the potential difference in phenotypic resistance in in vitro compared to murine in vivo models using CFU data alone or CFU together with most probable number (MPN) data following resuscitation with culture supernatant. Predictions of in vitro and in vivo phenotypic resistance i.e. persisters, using the Multistate Tuberculosis Pharmacometric (MTP) model framework was evaluated based on bacterial cultures grown with and without drug exposure using CFU alone or CFU plus MPN data. Phenotypic resistance and total bacterial number in in vitro natural growth observations, i.e. without drug, was well predicted by the MTP model using only CFU data. Capturing the murine in vivo total bacterial number and persisters during natural growth did however require re-estimation of model parameter using both the CFU and MPN observations implying that the ratio of persisters to total bacterial burden is different in vitro compared to murine in vivo. The evaluation of the in vitro rifampicin drug effect revealed that higher resolution in the persister drug effect was seen using CFU and MPN compared to CFU alone although drug effects on the other bacterial populations were well predicted using only CFU data. The ratio of persistent bacteria to total bacteria was predicted to be different between in vitro and murine in vivo. This difference could have implications for subsequent translational efforts in tuberculosis drug development

Differential Regulation of Circulating Levels of Molecular Chaperones in Patients Undergoing Treatment for Periodontal Disease

British Heart Foundation (grant PG/03/029

Curcumin induces apoptosis of upper aerodigestive tract cancer cells by targeting multiple pathways

Curcumin, a natural compound isolated from the Indian spice Haldi or curry powder , has been used for centuries as a traditional remedy for many ailments. Recently, the potential use of curcumin in cancer prevention and therapy urges studies to uncover the molecular mechanisms associated with its anti-tumor effects. In the current manuscript, we investigated the mechanism of curcumin-induced apoptosis in upper aerodigestive tract cancer cell lines and showed that curcumin-induced apoptosis is mediated by the modulation of multiple pathways such as induction of p73, and inhibition of p-AKT and Bcl-2. Treatment of cells with curcumin induced both p53 and the related protein p73 in head and neck and lung cancer cell lines. Inactivation of p73 by dominant negative p73 significantly protected cells from curcumin-induced apoptosis, whereas ablation of p53 by shRNA had no effect. Curcumin treatment also strongly inhibited p-AKT and Bcl-2 and overexpression of constitutively active AKT or Bcl-2 significantly inhibited curcumin-induced apoptosis. Taken together, our findings suggest that curcumin-induced apoptosis is mediated via activating tumor suppressor p73 and inhibiting p-AKT and Bcl-2

GPU Concurrency: Weak Behaviours and Programming Assumptions

Concurrency is pervasive and perplexing, particularly on graphics processing units (GPUs). Current specifications of languages and hardware are inconclusive; thus programmers often rely on folklore assumptions when writing software. To remedy this state of affairs, we conducted a large empirical study of the concurrent behaviour of deployed GPUs. Armed with litmus tests (i.e. short concurrent programs), we questioned the assumptions in programming guides and vendor documentation about the guarantees provided by hardware. We developed a tool to generate thousands of litmus tests and run them under stressful workloads. We observed a litany of previously elusive weak behaviours, and exposed folklore beliefs about GPU programming---often supported by official tutorials---as false. As a way forward, we propose a model of Nvidia GPU hardware, which correctly models every behaviour witnessed in our experiments. The model is a variant of SPARC Relaxed Memory Order (RMO), structured following the GPU concurrency hierarchy

Significance of herpesvirus immediate early gene expression in cellular immunity to cytomegalovirus infection

Interstitial pneumonia linked with reactivation of latent human cytomegalovirus due to iatrogenic immunosuppression can be a serious complication of bone marrow transplantation therapy of aplastic anaemia and acute leukaemia1. Cellular immunity plays a critical role in the immune surveillance of inapparent cytomegalovirus infections in man and the mouse1−7. The molecular basis of latency, however, and the interaction between latently or recurrently infected cells and the immune system of the host are poorfy understood. We have detected a so far unknown antigen in the mouse model. This antigen is found in infected cells in association with the expression of the herpesvirus 'immediate early' genes and is recognized by cytolytic T lymphocytes (CTL)8. We now demonstrate that an unexpectedly high proportion of the CTL precursors generated in vivo during acute murine cytomegalovirus infection are specific for cells that selectively synthesize immediate early proteins, indicating an immunodominant role of viral non-structural proteins

Multi-category Bangla News Classification using Machine Learning Classifiers and Multi-layer Dense Neural Network

Online and offline newspaper articles have become an integral phenomenon to our society. News articles have a significant impact on our personal and social activities but picking a piece of an appropriate news article is a challenging task for users from the ocean of sources. Recommending the appropriate news category helps find desired articles for the readers but categorizing news article manually is laborious, sluggish and expensive. Moreover, it gets more difficult when considering a resource-insufficient language like Bengali which is the fourth most spoken language of the world. However, very few approaches have been proposed for categorizing Bangla news articles where few machine learning algorithms were applied with limited resources. In this paper, we accentuate multiple machine learning approaches including a neural network to categorize Bangla news articles for two different datasets. News articles have been collected from the popular Bengali newspaper Prothom Alo to build Dataset I and dataset II has been gathered from the famous machine learning competition platform Kaggle. We develop a modified stop-word set and apply it in the preprocessing stage which leads to significant improvement in the performance. Our result shows that the Multi-layer Neural network, Naïve Bayes and support vector machine provide better performance. Accuracy of 94.99%, 94.60%, 95.50% has been achieved for SVM, Logistic regression and Multi-layer dense neural network, respectively