Present Status of Antileishmanial Vaccines

The term leishmaniasis refers collectively to various clinical syndromes that are caused by obligate intracellular protozoa of the genus Leishmania. Approximately 350 million people in 8 countries are estimated to be threatened by the disease [1]. The World Health Organization estimated that there are 12 million cases of all forms of leishmaniasis worldwide, with over 500,000 new cases of visceral disease occurring each year [1]. Most of the drugs commonly used to treat different forms of leishmaniasis are toxic and have unacceptable side effects. Moreover, cases of drug resistant leishmaniasis are on the rise. Due to nonexistence of effective vaccine to date, improved immunoprophylactic approaches still remain desirable to combat leishmaniasis. Antileishmanial vaccines developed around the globe are discussed

Interaction of Leishmania parasites with dendritic cells and its functional consequences

Interaction between dendritic cells (DC) and T cells is essential for the generation of cell mediated immunity and thus DC play a critical role in the initiation of immune responses against Leishmania parasites. Although macrophages (Mf) are the major targets of all species of Leishmania, a number of studies demonstrated the infection of DC by Leishmania. DC specific intracellular adhesion molecule 3-grabbing nonintegrin (DC-SIGN), has been reported to be the receptor for Leishmania amastigotes. The functional consequences in DC after Leishmania infections appear to depend on species of Leishmania. Some species of Leishmania enhance the surface expression of co-stimulatory molecules and CD40-ligand-induced IL-12 production in DC. On the other hand other species down-regulate costimulatory molecules and inhibit IL-12 production. The intrinsic differences among Leishmania species with regard to alteration of cell surface molecules and IL-12 production in DC may contribute to the healing and non-healing forms of the disease

Reducing parameter value uncertainty in discrete Bayesian network learning: a semantic fuzzy Bayesian approach

Bayesian network has gained increasing popularity among the data scientists and research communities, because of its inherent capability of capturing probabilistic information and reasoning with uncertain knowledge. However, the discrete Bayesian learning, with continuous and categorical variables, often shows poor performance because of parameter value uncertainty, arising due to strict boundary value of the discretized data and lack of knowledge on domain semantics. In this work, we propose semFBnet, a variant of Bayesian network with incorporated fuzziness and semantic knowledge, to reduce the uncertainty during parameter learning. The performance of semFBnet has been validated with prediction of daily meteorological conditions in two states of India, namely West Bengal and Delhi, for the years 2015 and 2016, respectively. The study of Dawid-Sebastiani score and the confidence interval analysis, in comparison with the state-of-theart and benchmark prediction techniques, demonstrate the effectiveness of the proposed semFBnet in reducing parameter value uncertainty

Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts

With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data

Investigation on bacterial adhesion and colonisation resistance over laser-machined micro patterned surfaces

Micro–nano patterns created directly over solid surfaces to combat microbial activity help in preventing hospital-acquired infections. This Letter is focused on defining surface topologies by laser patterning over solid surfaces. Studies on designing surface topologies and bacterial culture have been carried out and the feasibility of micro scale features in restricting bacterial growth has been investigated. The effects of the engineered roughness index and contact angle are discussed. Contact angle measurement over patterned surfaces using a novel computer vision-based technique is demonstrated and the effect of contact angle on bacterial adhesion has been presented. The results obtained show that the designed micro scale geometries can effectively reduce the growth of bacteria on the said surfaces

Statistical and Machine Learning Models for Remote Sensing Data Mining—Recent Advancements

During the last few decades, the remarkable progress in the field of satellite remote sensing (RS) technology has enabled us to capture coarse, moderate to high-resolution earth imagery on weekly, daily, and even hourly intervals [...

Statistical and Machine Learning Models for Remote Sensing Data Mining—Recent Advancements

During the last few decades, the remarkable progress in the field of satellite remote sensing (RS) technology has enabled us to capture coarse, moderate to high-resolution earth imagery on weekly, daily, and even hourly intervals [...

Dendritic Cell-Based Immunotherapy Combined with Antimony-Based Chemotherapy Cures Established Murine Visceral Leishmaniasis

Dendritic cells (DCs) have been proposed to play a critical role as adjuvants in vaccination and immunotherapy. In this study we evaluated the combined effect of soluble Leishmania donovani Ag (SLDA)-pulsed syngeneic bone marrow-derived DC-based immunotherapy and antimony-based chemotherapy for the treatment of established murine visceral leishmaniasis. Three weekly injections of SLDA-pulsed DCs into L. donovani-infected mice reduced liver and splenic parasite burden significantly, but could not clear parasite load from these organs completely. Strikingly, the conventional antileishmanial chemotherapy (sodium antimony gluconate) along with injections of SLDA-pulsed DCs resulted in complete clearance of parasites from both these organs. Repetitive in vitro stimulation of splenocytes from uninfected or L. donovani-infected mice with SLDA-pulsed DCs led to the emergence of CD4� T cells with characteristics of Th1 cells. Our data indicate that DC-based immunotherapy enhances the in vivo antileishmanial potential of antimony or vice versa