Empagliflozin: a wonder drug in preventing diabetic nephropathy and cardiovascular effects

Diabetes, a terrible disease, with highest incidence in world produces many complications which will end up in dreadful situations. Diabetes nephropathy is one of the complications which lead to dialysis, renal replacement. empagliflozin, sodium glucose cotransporter 2 inhibitors decreases plasma blood glucose level, HbA1C and weight by excreting glucose in urine. It by decreasing HbA1C results in decrease in complications of diabetes due to high blood sugar and fluctuating blood sugars. It decreases the blood pressure, arterial stiffness and vascular resistance. It decreases the risk factors of diabetic nephropathy like hyperglycemia and high blood pressure. It also decreases the diabetes-related glomerular hypertrophy, markers of renal inflammation, as well as mesangial matrix expansion and thus ameliorates the early signs of diabetic nephropathy. The efficacy in decreasing blood glucose levels was confirmed in four randomized placebo controlled studies. The safety concern was seen in studies were urinary and genital tract infections. It thus proves to be an efficient option for diabetes in preventing diabetes nephropathy and cardiovascular effects

Microfluidic 3D Gradient Generator for Studying Tubulogenesis

Tubulogenesis and Angiogenesis is an important process in the formation of new blood vessels, and play a vital role in many physiological and pathological processes, such as cancer metastasis. To study this process in the laboratory, versatile, reliable and affordable assays are essential. The objective of this thesis work was to develop a microfluidic three-dimensional (3D) gradient device, which embedded collagen within the device for generating chemical gradient through it, and investigated it as a useful research tool for angiogenesis study with endothelial cells forming branching tubes inside the collagen.;With the enormous growth of microfluidic technologies, the scope for creating more realistic in vitro cell angiogenesis assays that replicate many aspects of the true in vivo microenvironment has increased. Although few conventional assays like rabbit ear chamber assay and chick chorio allantoic membrane assay are available in market, they set their own limitations for further studies. For instance, high in cost, lack of precise gradient control and characterization, limitation in mimicking the micro environment etc. Here, in this thesis work we introduced a microfluidic 3D gradient device and angiogenesis study assay that serves as a versatile single platform for the study of angiogenesis which is crucial step in the study of vascular biology and it related diseases. Briefly, a uniform collagen layer, which served as an extra cellular matrix (ECM), gave a precise control of generating a chemical gradient over a long period of time and offered an excellent monitoring capability with response to observing endothelial cells\u27 formation of tube structures. Human umbilical vein endothelial cells (HUVEC) were cultured inside the corresponding location of a micro channel for 4-5 days, and their responses to the quantified gradient of vascular endothelial growth factor (VEGF) were examined. Immunofluorescence staining and confocal imaging were made for detailed study on formed tubing. These results suggested that the microfluidic 3D gradient device can conveniently generate a stable chemical gradient and provide an easy way for the study of angiogenesis

Basis for the role of NF-kB in Inflammation & Cancer

The 19th-century pathologist Virchow noted an association between inflammation and cancer. This initial observation has been supported by recent epidemiological data, which has lead to the estimation that 20% of cancers are linked to chronic infections and persistent inflammation. Primary examples are gastric cancer, hepatocellular carcinoma (HCC), viral hepatitis and colitis- associated cancer. The NF-κB signalling pathway has particular relevance for several liver diseases including hepatitis, liver fibrosis and hepatocellular carcinoma. Constitutive NF-κB activation is a hallmark for many cancers and a major link between inflammation and cancer is provided by NF-κB transcription factor. Inhibition of apoptosis is perhaps the most obvious way through which NF-κB signalling promotes the development of cancer. Numerous NF-κB target genes, such as Gadd45β, prevent apoptosis and have been shown to be required for liver regeneration post-partial hepatectomy. The precise mechanism by which NF-κB regulates the inflammatory mechanisms that drive tumourigenesis, however are poorly understood. In order to elucidate these mechanisms, we propose to examine the role of Gadd45β in the NF-κB mediated link between inflammation and cancer. This study reports that Gadd45β deficient mice are more resistant to diethylnitrosamine (DEN) induced hepatocellular carcinogenesis. Gadd45β knockout (KO) mice developed HCC approximately four-fold lower than in control mice. Tumour number and maximum tumour size were also markedly reduced in KO mice. To further investigate in which cell types Gadd45β exerts its tumourigenic action we used bone marrow chimeric mice. Results have demonstrated that Gadd45β exerts its tumourigenic action primarily within the bone marrow derived cells but not in hepatocytes specifically during tumour progression stage than in tumour initiation stage. Furthermore, bone marrow-derived macrophages from KO mice preferentially polarised more towards macrophage M1 like phenotype that promotes anti-tumour immunity and inflammation. Thus, here we report a previously unidentified mechanism by which NF-κB linked inflammation to cancer and identified Gadd45β as a putative mediator of the NF-κB’s tumourigenic activity.Open Acces

Phase Evolution During Mechanical Milling of Pre-alloyed Gas Atomized Maraging Steel Powders and Magnetic Characterization

Maraging steels are an important class of high strength steels that exhibit an exciting combination of magnetic and mechanical properties. Past research work, specifically on the magnetic properties, focused on improving the magnetic properties of the bulk form of the steel, fabricated by conventional materials processing and manufacturing. With the recent focus towards additive manufacturing, it is imperative to investigate the structure and magnetic properties of the maraging steel powder and the influence of temperature. In this thesis work, firstly, the structural and magnetic characterization of a commercially available pre-alloyed gas atomized powder was investigated. It comprised of primarily the martensite phase (α) and a small amount of austenite (γ). The powder particle size characteristics, D90 of the as-received powder was estimated as ~21 μm. The saturation magnetization (MS), intrinsic coercivity (HCI), and remanent magnetization (MR) of the as-received powder, at ambient temperature, was ~176 Am2/kg, ~3 kA/m, and ~1.4 Am2/kg, respectively. Thermal treatment of the powder up to 900 K for ~1 h showed an inappreciable change in MS, while the coercivity decreased, suggesting good magnetic properties and promising opportunities to reuse the powder. Subsequently, phase evolution during mechanical milling of the pre-alloyed powder was investigated. Powder milled from 3 h to 8 h comprised nanocrystalline martensitic phase. The estimated grain size was as low as ~20 nm. The MS, HCI, and MR ranged between ~164 Am2/kg and ~169 Am2/kg, ~4.9 kA/m and ~6.7 kA/m, and ~3.4 Am2/kg to ~3.9 Am2/kg, respectively. Milling more than 8 h resulted in the formation of austenite and extraneous intermetallic phases, resulting in the reduction of MS and increase in HCI. At cryogenic temperatures (60 K-300 K), MS (0) (MS at 0 K) and maximum magnetic moment per atom (μH) of the nanocrystalline milled maraging powders were ~ 178 Am2/kg and ~ 1.83 μB, respectively. The thermally treated maraging steel powders retained the nanostructure, and their MS and HCI were comparable to as-received powder.Master of Science in EngineeringMechanical Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/150650/1/Ganesh Varma Thotakura - Final Thesis.pdfDescription of Ganesh Varma Thotakura - Final Thesis.pdf : Thesi

Assessment of knowledge, attitude and practice in pharmacovigilance among clinical post graduate students in a teaching hospital, Vizianagaram, Andhra Pradesh, India

Background: Adverse drug reactions (ADR) are global problem causing morbidity and mortality. Good pharmacovigilance (PV) programme can address this problem. Under reporting of ADR is one of the obstacles for good PV. In order to achieve this, health professionals need to have enough Knowledge, attitude and practice (KAP) of ADR`s. The present study was conducted to assess KAP about PV among post graduate (PG) students.Methods: A self administered questionnaire validated by Lynn M consisting of 22 questions covering knowledge, attitude & practice about PV was distributed among PG students of clinical departments of MIMS College. Answering of the questionnaires was supervised directly. Filled questionnaires were analyzed by using Microsoft Excel spread sheet.Results: Evaluation showed an average of 52.3% correct and 47.7% incorrect knowledge about ADR`s and PV.50% students are not sure regarding occurrence of ADR.90% students are not been trained upon reporting of ADR`s. Our study found out that PG students have better attitude towards PV, but have improper knowledge & less awareness about PV. We also found lack of practice among the students.Conclusions: Imparting knowledge and awareness of PV among the PG students by means of continuous educational intervention can create better practice among PG students

Assessment of Reliability of Composite Power System Including Smart Grids

The large service interruptions of power supply in the transmission system have significant impact on modern society. The aim of the power system engineers is to prevent and mitigate such events with optimal decisions in design, planning, operation and maintenance. Due to the rapid growth in the power demand and competitive power market scenario, the transmission and distribution systems are frequently being operated under heavily loaded conditions. This tends to make failure of components more frequent in the power system necessitating large downtime to repair or replace the equipment. A majority of the service interruptions are happening due to lack of proper planning and operation of power system. Therefore, complete reliability assessment in generation, transmission and distribution systems is needed at the planning stage. The reliability assessment in smart grids is very much beneficial to the power operator and reduces the risk of grid failure due to failure of major components in power systems. This chapter is confined to composite power system reliability assessment. The composite power system combines both the generation and transmission systems’ adequacy. The generation system in the composite power system includes both conventional and renewable sources. The composite power system reliability assessment is quite difficult due to the large number of equipment, interconnected network topology and uncertainties in generation capacity. The reliability assessment concentrates mainly on the use of probabilistic states of components in generation and transmission systems to evaluate the overall reliability. This analysis will result in a cost-effective system configuration to provide continuous power supply to the consumers at reasonable cost. The reliability level of the system is measured by the defined indices. One of these indices is the probability of average power availability at load bus. This reliability assessment mainly focuses on development of methods to evaluate the probability of average power availability at load buses for a specified system configuration. This chapter discusses the two main techniques called node elimination method and modified minimal cut set method

The regulation of the JNK cascade and programmed cell death by NF-κB: mechanisms and functions

The nuclear factor κB (NF-κB) family is an evolutionarily conserved family of transcription factors that play a central role in immune and inflammatory responses. They also play a pivotal role in cell survival, whereby activation of NF-κB antagonizes programmed cell death induced by tumor necrosis factor receptors and other cell death signals. The prosurvival function of NF-κB has been implicated in a wide range of biological processes, including the development and homeostasis of the immune system and liver. It has also been implicated in the pathogenesis of numerous diseases, including cancer, chronic inflammation, and certain hereditary disorders. The protective activity of NF-κB can also hamper tumor cell killing inflicted by radiation or chemotherapeutic drugs, thereby promoting resistance to cancer treatments. This prosurvival activity of NF-κB involves the suppression of sustained c-Jun N-terminal kinase (JNK) activation and of the accumulation of cytotoxic reactive oxygen species. NF-κB mediates this function by inducing the transcription of target genes, whose products inhibit the JNK signaling pathway and suppress accumulation of reactive oxygen species through their antioxidant functions. The development of specific inhibitors that target the critical downstream NF-κB-regulated genes that promote survival in cancer and other diseases potentially holds a key to developing specific and effective therapeutic strategies to combat these disorders

Purging of silence for robust speaker identification in colossal database

The aim of this work is to develop an effective speaker recognition system under noisy environments for large data sets. The important phases involved in typical identification systems are feature extraction, training and testing. During the feature extraction phase, the speaker-specific information is processed based on the characteristics of the voice signal. Effective methods have been proposed for the silence removal in order to achieve accurate recognition under noisy environments in this work. Pitch and Pitch-strength parameters are extracted as distinct features from the input speech spectrum. Multi-linear principle component analysis (MPCA) is is utilized to minimize the complexity of the parameter matrix. Silence removal using zero crossing rate (ZCR) and endpoint detection algorithm (EDA) methods are applied on the source utterance during the feature extraction phase. These features are useful in later classification phase, where the identification is made on the basis of support vector machine (SVM) algorithms. Forward loking schostic (FOLOS) is the efficient large-scale SVM algorithm that has been employed for the effective classification among speakers. The evaluation findings indicate that the methods suggested increase the performance for large amounts of data in noise ecosystems

Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions

Homology based HP-PPIs predicted from HPIDB. Table S9. Pathway enrichment analysis of host genes from DAVID functional annotation tool. Table S10. Gene ontology report of host genes. Table S11. Disease enrichment analysis of host genes participated in HP-PPIs. Table S12. Total functional annotation cluster analysis of host genes. (XLSX 99 kb

Multi-class SVM based C3D Framework for Real-Time Anomaly Detection

166-172The conventional multi-class anomaly detection models are independent of noise elimination and feature segmentation due to large number of feature space and training images. As the number of human anomaly classes is increasing, it is difficult to find the multi-class anomaly due to high computational memory and time. In order to improve the multi-class human anomaly detection process, an advanced multi-class segmentation-based classification model is designed and implemented on the different human anomaly action databases. In the proposed model, a hybrid filtered based C3D framework is used to find the essential key features from the multiple human action data and an ensemble multi-class classification model is implemented in order to predict the new type of actions with high accuracy. Experimental outcomes proved that the proposed multi- class classification C3D model has better human anomaly detection rate than the traditional multi-class segmentation models