Urokinase Plasminogen Activator Inhibits HIV Virion Release from Macrophage-Differentiated Chronically Infected Cells via Activation of RhoA and PKCε

HIV replication in mononuclear phagocytes is a multi-step process regulated by viral and cellular proteins with the peculiar feature of virion budding and accumulation in intra-cytoplasmic vesicles. Interaction of urokinase-type plasminogen activator (uPA) with its cell surface receptor (uPAR) has been shown to favor virion accumulation in such sub-cellular compartment in primary monocyte-derived macrophages and chronically infected promonocytic U1 cells differentiated into macrophage-like cells by stimulation with phorbol myristate acetate (PMA). By adopting this latter model system, we have here investigated which intracellular signaling pathways were triggered by uPA/uPAR interaction leading the redirection of virion accumulation in intra-cytoplasmic vesicles.uPA induced activation of RhoA, PKCδ and PKCε in PMA-differentiated U1 cells. In the same conditions, RhoA, PKCδ and PKCε modulated uPA-induced cell adhesion and polarization, whereas only RhoA and PKCε were also responsible for the redirection of virions in intracellular vesicles. Distribution of G and F actin revealed that uPA reorganized the cytoskeleton in both adherent and polarized cells. The role of G and F actin isoforms was unveiled by the use of cytochalasin D, a cell-permeable fungal toxin that prevents F actin polymerization. Receptor-independent cytoskeleton remodeling by Cytochalasin D resulted in cell adhesion, polarization and intracellular accumulation of HIV virions similar to the effects gained with uPA.These findings illustrate the potential contribution of the uPA/uPAR system in the generation and/or maintenance of intra-cytoplasmic vesicles that actively accumulate virions, thus sustaining the presence of HIV reservoirs of macrophage origin. In addition, our observations also provide evidences that pathways controlling cytoskeleton remodeling and activation of PKCε bear relevance for the design of new antiviral strategies aimed at interfering with the partitioning of virion budding between intra-cytoplasmic vesicles and plasma membrane in infected human macrophages

Improving spatial agreement in machine learning-based landslide susceptibility mapping

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions

Attitude towards diabetes and social and family support among type 2 diabetes patients attending a tertiary-care hospital in Bangladesh: a cross-sectional study

Background: Bangladesh has been suffering from an epidemiological transition from infectious and maternal diseases to non-communicable lifestyle-related diseases like diabetes, cardiovascular diseases, cancers etc. The burden of diabetes has been increasing rapidly due to high incidence as well as poor glycemic control leading to various macro and micro-vascular complications. In this study, we aim to assess the attitude towards diabetes and social and family support among the Bangladeshi type 2 diabetic mellitus (T2DM) patients. Methods: This was a cross-sectional study among 144 patients with T2DM at the medicine outpatient department of Dhaka Medical College Hospital (DMCH) in Dhaka, Bangladesh between 1 July and 31 July 2014. Data collection was done by interviewing patients using structured questionnaire. Understanding diabetes, education/advice received, attitude towards diabetes, family and friend support were measured by validated scales adapted from diabetes care profile. Results: This study includes a total of 144 patients (101 males and 43 females) with type 2 diabetes aged between 20 and 84 years. 87 % of the patients had inadequate blood glucose control (fasting blood sugar &gt;7.2 mmol/L or &gt;130 mg/dl). Statistically significant differences were observed in the mean scores of various attitude scales (i.e. positive, negative, care ability and self-care adherence scale) among patients with adequate and inadequate blood glucose control (p &lt; 0.05). Statistically significant positive correlations were found between these three categories of social and family support. Self-satisfaction with diabetic care was significantly associated with adequate blood glucose control (p = 0.05). Conclusions: Positive attitude towards diabetes management and support from friends and family were associated with adequate diabetes management. Appropriate public health interventions should be designed to educate and motivate the family members to offer greater support to the diabetes patients.</p