Phytochemical screening and antibacterial effect of root extract of Boerhaavia diffusa L. (Family Nyctaginaceae)

Boerhaavia diffusa belonging to the family of the Nyctaginaceae is mainly a diffused perennial herbaceous creeping weed of India. The present study deals with the investigation of phytochemical analysis and evaluation of the antimicrobial activity of the aqueous and methanolic extract of the roots of Boerhaavia diffusa. The result revealed the presence of alkaloid, glycoside, saponins, flavonoids, polysaccharides, steroid and tannin in both the root extracts. B. diffusa root extract possesses antimicrobial activity as the zone of inhibition was observed for both gram positive as well as gram negative bacterial strains

Analysis of bioactive phytochemicals and evaluation of antioxidant activity of a medicinal plant, Boerhaavia diffusa L.

Boerhaavia diffusa L. (Family: Nyctaginaceae) commonly known as Punarnava is an herbaceous, spreading vine widely distributed in the tropical and subtropical regions in the world. The plants are a rich source of vitamins, minerals, protein and carbohydrate. The present study was carried out to determine the concentration of some bioactive phytochemicals (ascorbic acid,carotenoids, total phenolics, protein and carbohydrate) and their antioxidant activity in punarnava. Results showed the values for ascorbic acid (16.75±1.72 and 18.86±1.12 mg/100g of Fresh Weight), carotenoids (1.36±0.10 and 1.98±0.11 ìg/g of Fresh Weight), protein (122.975±6.27 and 134.45±6.23 mg/g of dry weight) and carbohydrate (56.67±5.77 and 60.11±5.23 mg/g of dry weight) for aqueous and methanolic of root extracts of B.diffusa respectively. Methanolic root extracts showed greater antioxidant activity than the aqueous extracts using DPPH method

Assessment of Disease Activity and Complications in Patients of Pulmonary Tuberculosis by High Resolution Computed Tomography

Background: Tuberculosis (TB) is a global health problem and the second most common infectious cause of death. High-resolution computed tomography (HRCT) is far more superior to chest radiography as well as conventional CT for analyzing the pulmonary parenchyma. This study aimed to evaluate the role of HRCT in pulmonary tuberculosis (PTB) with respect to disease activity and complication after anti-tubercular therapy (ATT). Methods: This prospective observational study was conducted in the Department of Radiodiagnosis, Teerthanker Mahaveer Medical College & Research Centre (TMMC&RC) for a period of 1.5 years. A total of 50 cases of newly diagnosed TB were included in the study and a standard six-month ATT was given to the patients. Pulmonary involvement was evaluated by HRCT (128 slice multi-detector PHILIPS INGENUITY CT scanner), twice for each patient (first scan after diagnosis and second after treatment completion). The acquired HRCT images were reconstructed on a highresolution lung algorithm and parenchymal, bronchial, and extra parenchymal findings were recorded systematically. Results: Out of the 50 patients, 5 died within two months of the initiation of treatment and four were lost to follow-up. Thus, post treatment follow-up sample size was reduced to 41 patients. Ill-defined nodules (96%), tree-in-bud pattern (74%), consolidation (86%), cavitary lesions (98%), and ground glass opacities (58%) were the main imaging features of active cases of TB on HRCT. Resolution to thin-walled cavitary lesions (36.5%), bronchiectasis (41.5%), and fibrotic (parenchymal) bands (66%) were common complications or sequelae which were observed after completion of treatment. Conclusion: HRCT thorax is a sensitive modality for evaluation of parenchymal and airway manifestations in cases of PTB and can aid in differentiation of active disease from healed disease. It allows early identification of post-treatment complications and sequelae in patients of PTB

A novel tissue-specific meta-analysis approach for gene expression predictions, initiated with a mammalian gene expression testis database

<p>Abstract</p> <p>Background</p> <p>In the recent years, there has been a rise in gene expression profiling reports. Unfortunately, it has not been possible to make maximum use of available gene expression data. Many databases and programs can be used to derive the possible expression patterns of mammalian genes, based on existing data. However, these available resources have limitations. For example, it is not possible to obtain a list of genes that are expressed in certain conditions. To overcome such limitations, we have taken up a new strategy to predict gene expression patterns using available information, for one tissue at a time.</p> <p>Results</p> <p>The first step of this approach involved manual collection of maximum data derived from large-scale (genome-wide) gene expression studies, pertaining to mammalian testis. These data have been compiled into a Mammalian Gene Expression Testis-database (MGEx-Tdb). This process resulted in a richer collection of gene expression data compared to other databases/resources, for multiple testicular conditions. The gene-lists collected this way in turn were exploited to derive a 'consensus' expression status for each gene, across studies. The expression information obtained from the newly developed database mostly agreed with results from multiple small-scale studies on selected genes. A comparative analysis showed that MGEx-Tdb can retrieve the gene expression information more efficiently than other commonly used databases. It has the ability to provide a clear expression status (transcribed or dormant) for most genes, in the testis tissue, under several specific physiological/experimental conditions and/or cell-types.</p> <p>Conclusions</p> <p>Manual compilation of gene expression data, which can be a painstaking process, followed by a consensus expression status determination for specific locations and conditions, can be a reliable way of making use of the existing data to predict gene expression patterns. MGEx-Tdb provides expression information for 14 different combinations of specific locations and conditions in humans (25,158 genes), 79 in mice (22,919 genes) and 23 in rats (14,108 genes). It is also the first system that can predict expression of genes with a 'reliability-score', which is calculated based on the extent of agreements and contradictions across gene-sets/studies. This new platform is publicly available at the following web address: <url>http://resource.ibab.ac.in/MGEx-Tdb/</url></p

Global mapping of binding sites for Nrf2 identifies novel targets in cell survival response through ChIP-Seq profiling and network analysis

The Nrf2 (nuclear factor E2 p45-related factor 2) transcription factor responds to diverse oxidative and electrophilic environmental stresses by circumventing repression by Keap1, translocating to the nucleus, and activating cytoprotective genes. Nrf2 responses provide protection against chemical carcinogenesis, chronic inflammation, neurodegeneration, emphysema, asthma and sepsis in murine models. Nrf2 regulates the expression of a plethora of genes that detoxify oxidants and electrophiles and repair or remove damaged macromolecules, such as through proteasomal processing. However, many direct targets of Nrf2 remain undefined. Here, mouse embryonic fibroblasts (MEF) with either constitutive nuclear accumulation (Keap1−/−) or depletion (Nrf2−/−) of Nrf2 were utilized to perform chromatin-immunoprecipitation with parallel sequencing (ChIP-Seq) and global transcription profiling. This unique Nrf2 ChIP-Seq dataset is highly enriched for Nrf2-binding motifs. Integrating ChIP-Seq and microarray analyses, we identified 645 basal and 654 inducible direct targets of Nrf2, with 244 genes at the intersection. Modulated pathways in stress response and cell proliferation distinguish the inducible and basal programs. Results were confirmed in an in vivo stress model of cigarette smoke-exposed mice. This study reveals global circuitry of the Nrf2 stress response emphasizing Nrf2 as a central node in cell survival response

Mapping of variations in child stunting, wasting and underweight within the states of India: the Global Burden of Disease Study 2000–2017

Background To inform actions at the district level under the National Nutrition Mission (NNM), we assessed the prevalence trends of child growth failure (CGF) indicators for all districts in India and inequality between districts within the states. Methods We assessed the trends of CGF indicators (stunting, wasting and underweight) from 2000 to 2017 across the districts of India, aggregated from 5 × 5 km grid estimates, using all accessible data from various surveys with subnational geographical information. The states were categorised into three groups using their Socio-demographic Index (SDI) levels calculated as part of the Global Burden of Disease Study based on per capita income, mean education and fertility rate in women younger than 25 years. Inequality between districts within the states was assessed using coefficient of variation (CV). We projected the prevalence of CGF indicators for the districts up to 2030 based on the trends from 2000 to 2017 to compare with the NNM 2022 targets for stunting and underweight, and the WHO/UNICEF 2030 targets for stunting and wasting. We assessed Pearson correlation coefficient between two major national surveys for district-level estimates of CGF indicators in the states. Findings The prevalence of stunting ranged 3.8-fold from 16.4% (95% UI 15.2–17.8) to 62.8% (95% UI 61.5–64.0) among the 723 districts of India in 2017, wasting ranged 5.4-fold from 5.5% (95% UI 5.1–6.1) to 30.0% (95% UI 28.2–31.8), and underweight ranged 4.6-fold from 11.0% (95% UI 10.5–11.9) to 51.0% (95% UI 49.9–52.1). 36.1% of the districts in India had stunting prevalence 40% or more, with 67.0% districts in the low SDI states group and only 1.1% districts in the high SDI states with this level of stunting. The prevalence of stunting declined significantly from 2010 to 2017 in 98.5% of the districts with a maximum decline of 41.2% (95% UI 40.3–42.5), wasting in 61.3% with a maximum decline of 44.0% (95% UI 42.3–46.7), and underweight in 95.0% with a maximum decline of 53.9% (95% UI 52.8–55.4). The CV varied 7.4-fold for stunting, 12.2-fold for wasting, and 8.6-fold for underweight between the states in 2017; the CV increased for stunting in 28 out of 31 states, for wasting in 16 states, and for underweight in 20 states from 2000 to 2017. In order to reach the NNM 2022 targets for stunting and underweight individually, 82.6% and 98.5% of the districts in India would need a rate of improvement higher than they had up to 2017, respectively. To achieve the WHO/UNICEF 2030 target for wasting, all districts in India would need a rate of improvement higher than they had up to 2017. The correlation between the two national surveys for district-level estimates was poor, with Pearson correlation coefficient of 0.7 only in Odisha and four small north-eastern states out of the 27 states covered by these surveys. Interpretation CGF indicators have improved in India, but there are substantial variations between the districts in their magnitude and rate of decline, and the inequality between districts has increased in a large proportion of the states. The poor correlation between the national surveys for CGF estimates highlights the need to standardise collection of anthropometric data in India. The district-level trends in this report provide a useful reference for targeting the efforts under NNM to reduce CGF across India and meet the Indian and global targets. Keywords Child growth failureDistrict-levelGeospatial mappingInequalityNational Nutrition MissionPrevalenceStuntingTime trendsUnder-fiveUndernutritionUnderweightWastingWHO/UNICEF target

Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2+/+ and Nrf2−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease

Deadlock Prevention Algorithm in Grid Environment

Deadlock is a highly unfavourable situation, the deadlock problem becomes further complicated if the underlying system is distributed. Deadlocks in distributed systems are similar to deadlocks in single processor systems, only worse. They are harder to avoid, prevent or even detect. They are hard to cure when tracked down because all relevant information is scattered over many machines.In deadlock situations the whole system or a part of it remains indefinitely blocked and cannot terminate its task. Therefore it is highly important to develop efficient control scheme to optimize the system performance while preventing deadlock situations.In this research paper, a new deadlock prevention algorithm have been offered with the emergence of grid computing. The main objective of paper is to prevent deadlock problem in Grid environment in order to preserve the data consistency and increase the throughput by maximizing the availability of resources and to ensure that all the resources available in the grid are effectively utilized