Antioxidative Defense Responses to lead-induced Oxidative Stress in Glycine max L. CV. Merrill grown in Different pH Gradient

Physiological and biochemical changes as well as the activities of anti-oxidative enzymes under lead (Pb2+) phytotoxicity were investigated in 20 days old soybean (Glycine max L.) seedlings grown hydroponically in the laboratory under different pH conditions. The rapid uptake of Pb 2+ was observed immediately after the start of treatment. The quantity of accumulation of Pb2+ was much higher in roots than in shoots, its level rising with increasing pH from 3.0 to 8.0 . Not only that, an oxidative stress conditions were observed due to increased level of superoxide anion radical and hydrogen peroxide in shoots and root cells of 20 days old seedlings when treated with Pb(NO3)2 at a concentration of 0, 500, 1000 and 2000 μM. Spectrometric assays of seedlings showed increased level of activities of antioxidant enzymes like catalase, peroxidase and glutathione reductase. The presence of thiobarbituric acid reacting substances (TBARS) indicates the enhanced lipid peroxidation compared to controls. The alteration in the activities of the antioxidant enzymes and the induction of lipid peroxidation reflects the presence of Pb2+, which may cause oxidative stress

Analysis of labor induction in a tertiary care hospital

Background: Induction of labour (IOL) is a very common labour room procedure. Although labor is a natural physiological process, deliberate intervention in the form of induction may be required in many instances. It is needed in almost 20% of pregnant women for a variety of indications. The objective is to evaluate indications, different methods, and feto-maternal outcome of induced labour in tertiary care hospital.Methods: This was a retrospective study of IOL conducted in the department of obstetrics and gynecology, Shri Guru Ram Rai institute of medical and health sciences, Dehradun, Uttarakhand. Women who underwent IOL beyond 28 weeks gestation with single cephalic presentation with no contraindication for vaginal birth were included in the study. Statistical analysis was done with Microsoft excel.Results: A total of 1532 women delivered in the hospital during the study period. Among them, 498 women were induced (32.5%). Most common method of induction was misoprostol (40.36%) followed by prostaglandin E2 gel (26.90%).  Out of 498 inductions, 377 women delivered vaginally making success of induction around 75.70%. Among them, 335 women had normal delivery (67.26%) and 42 women required instrumental delivery (8.4%) and 121 women underwent lower segment caesarean section (LSCS) (24.29%).Conclusions: Elective inductions of labor in properly selected indications at optimized timings aid in achieving a favorable maternal and fetal outcome. Methods of inductions, timing and intrapartum monitoring plays an important role in influencing obstetric outcome

Task Relationship Modeling in Lifelong Multitask Learning

Multitask Learning is a learning framework which explores the concept of sharing training information among multiple related tasks to improve the generalization error of each task. The benefits of multitask learning have been shown both empirically and theoretically. There are a number of fields that benefit from multitask learning such as toxicology, image annotation, compressive sensing etc. However, majority of multitask learning algorithms make a very important key assumption that all the tasks are related to each other in a similar fashion in multitask learning. The users often do not have the knowledge of which tasks are related and train all tasks together. This results in sharing of training information even among the unrelated tasks. Training unrelated tasks together can cause a negative transfer and deteriorate the performance of multitask learning. For example, consider the case of predicting in vivo toxicity of chemicals at various endpoints from the chemical structure. Toxicity at all the endpoints are not related. Since, biological networks are highly complex, it is also not possible to predetermine which endpoints are related. Training all the endpoints together may cause a negative effect on the overall performance. Therefore, it is important to establish the task relationship models in multitask learning. Multitask learning with task relationship modeling may be explored in three different settings, namely, static learning, online fixed task learning and most recent lifelong learning. The multitask learning algorithms in static setting have been present for more than a decade and there is a lot of literature in this field. However, utilization of task relationships in multitask learning framework has been studied in detail for past several years only. The literature which uses feature selection with task relationship modeling is even further limited. For the cases of online and lifelong learning, task relationship modeling becomes a challenge. In online learning, the knowledge of all the tasks is present before starting the training of the algorithms, and the samples arrive in online fashion. However, in case of lifelong multitask learning, the tasks also arrive in an online fashion. Therefore, modeling the task relationship is even a further challenge in lifelong multitask learning framework as compared to online multitask learning. The main contribution of this thesis is to propose a framework for modeling task relationships in lifelong multitask learning. The initial algorithms are preliminary studies which focus on static setting and learn the clusters of related tasks with feature selection. These algorithms enforce that all the tasks which are related select a common set of features. The later part of the thesis shifts gear to lifelong multitask learning setting. Here, we propose learning functions to represent the relationship between tasks. Learning functions is faster and computationally less expensive as opposed to the traditional manner of learning fixed sized matrices for depicting the task relationship models

Predictive Toxicology: Modeling Chemical Induced Toxicological Response Combining Circular Fingerprints with Random Forest and Support Vector Machine

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.Modern drug discovery and toxicological research are under pressure, as the cost of developing and testing new chemicals for potential toxicological risk is rising. Extensive evaluation of chemical products for potential adverse effects is a challenging task, due to the large number of chemicals and the possible hazardous effects on human health. Safety regulatory agencies around the world are dealing with two major challenges. First, the growth of chemicals introduced every year in household products and medicines that need to be tested, and second the need to protect public welfare. Hence, alternative and more efficient toxicological risk assessment methods are in high demand. The Toxicology in the 21st Century (Tox21) consortium a collaborative effort was formed to develop and investigate alternative assessment methods. A collection of 10,000 compounds composed of environmental chemicals and approved drugs were screened for interference in biochemical pathways and released for crowdsourcing data analysis. The physicochemical space covered by Tox21 library was explored, measured by Molecular Weight (MW) and the octanol/water partition coefficient (cLogP). It was found that on average chemical structures had MW of 272.6 Daltons. In case of cLogP the average value was 2.476. Next relationships between assays were examined based on compounds activity profiles across the assays utilizing the Pearson correlation coefficient r. A cluster was observed between the Androgen and Estrogen Receptors and their ligand bind domains accordingly indicating presence of cross talks among the receptors. The highest correlations observed were between NR.AR and NR.AR_LBD, where it was r = 0.66 and between NR.ER and NR.ER_LBD, where it was r = 0.5. Our approach to model the Tox21 data consisted of utilizing circular molecular fingerprints combined with Random Forest and Support Vector Machine by modeling each assay independently. In all of the 12 sub-challenges our modeling approach achieved performance equal to or higher than 0.7 ROC-AUC showing strong overall performance. Best performance was achieved in sub-challenges NR.AR_LBD, NR.ER_LDB and NR.PPAR_gamma, where ROC-AUC of 0.756, 0.790, and 0.803 was achieved accordingly. These results show that computational methods based on machine learning techniques are well suited to support and play critical role in toxicological research

Scavenging of nickel and chromium toxicity in Aulosira fertilissima by immobilization: Effect on nitrogen assimilating enzymes

The ubiquity of heavy metals in the biosphere results in the introduction of high amounts of toxic metals into the food chain from various sources. In the present study, one of the strongest nitrogen fixing cyanobacterium of the rice fields, Aulosira fertilissima , was subjected to nickel and chromium stress and the ameliorating effect of immobilization was investigated. Cell immobilization could protect the organism's growth against the toxicity of both heavy metals at LC50 as compared to lethal concentrations. The nitrate reductase activity in free cells treated with the metals was substantially inhibited but immobilized cells treated with 0.1 ppm nickel was not affected by the metal treatment. Cell immobilization also resulted in a significant protection against sub-lethal concentration of chromium but to a lesser degree than it did with sub- lethal levels of nickel. Control immobilized cells also had higher Nitrogenase activity than control free cells. Nickel and chromium addition markedly decreased the enzyme activity in free cells but immobilized cells exposed to sublethal concentrations of both metals could overcome this decrease. Glutamine synthetase showed similar response under immobilized conditions compared to free cells with both metals. The addition of algal filtrate in 3:1 ratio further increased the nitrogenase activity compared with immobilized cells treated with sublethal doses of both metals. Immobilization facilitated higher uptake of nickel as compared to chromium. The observations of the present study clearly demonstrate the protective effect of immobilization on Aulosira fertilissima against Nickel and chromium toxicity. Rice field ecosystem thus possess a bidirectional natural metal ameliorating system where Aulosira mats act as a naturally immobilized system and the decay of Aulosira along with other cyanobacteria act as natural chelators protecting the rice plants from deleterious effects of the heavy metals. Most importantly is that the immobilization process protects the cyanobacterial nitrogen fixing process allowing it to maintain nitrogen economy of the fields in spite of the presence of heavy metals

Association of body mass index, hand grip strength and quality of life with response to anti-tubercular therapy in adult patients of pulmonary and extra-pulmonary tuberculosis

Background: Monitoring of anti-tubercular therapy (ATT) response necessary for successful completion of ATT regimen. Presently available monitoring tools are invasive and have limitations. This study undertaken to see whether non-invasive tools like body mass index (BMI), hand grip strength (HGS) and quality of life (QOL) can serve as a reliable tool for monitoring ATT response.Methods: The 50 patients of tuberculosis were monitored for BMI, HGS and QOL via WHOQOL-BREF questionnaire and analyzed at baseline, 2 months and 6 months of starting ATT.Results: BMI HGS increased significantly at 2 months and 6 months compared to baseline with ATT. Physical and social domain of WHOQOL-BREF increased at 2 and 6 months with ATT, other domains shown no significant changes.Conclusions: Monitoring of BMI, handgrip strength and QOL can be a cost-effective tool for monitoring ATT response, both in pulmonary and extra pulmonary tuberculosis

A study to assess prevalence of treatment default among lung cancer patients registered at a tertiary care hospital

Background: Lung cancer is among the five main types of cancer leading to overall cancer mortality contributing about 1.3 million deaths/year globally. Completion of treatment among lung cancer patients is one of key factor for the survival and longevity of patients. So, we have tried to find out prevalence of treatment default through this study.Methods: This is a cross-sectional descriptive study (including retrospective secondary and prospective primary data) using data base of patients of primary lung cancer diagnosed between 1st January 2006 to 31st December 2012 in indoor and outdoor of department of Respiratory Medicine, J.L.N. Medical College, Ajmer, a tertiary level hospital and teaching center.Results: Incidence of lung cancer is significantly higher among young female (10.23%) as compared to young male (8.74 %). Whereas in older group number of male suffering from lung cancer than female. Total 269 (20.7%) patients defaulted from planned treatment and most of them ultimately drop-out from chemotherapy cycles. Intercycle delay of 2 weeks-1m commonly seen.Conclusions: It provides future implication to researchers to explore reasons of these defaults and drop outs so that more evidences can be generated in this direction for the ultimate betterment of lung cancer patients

Arbuscular Mycorrhiza-Associated Rhizobacteria and Biocontrol of Soilborne Phytopathogens

The mutualistic symbiosis of most land plants with arbuscular mycorrhizal (AM) fungi has been shown to favor mineral and water nutrition and to increase resistance to abiotic and biotic stresses. The main mechanisms involved in the control of the disease symptoms and intraradical proliferation of soilborne phytopathogens are due to root colonization with AM fungi. The role of the rhizobacteria is shown to be specifically associated with extraradical network of the AM and mycorrhizosphere. The mycorrhizosphere can form a favorable environment for microorganisms which have potentiality to act antagonistic to pathogen abundance. It makes an additional advantage in identifying rhizobacteria from AM fungi structures or mycorrhizosphere, which often lead to the isolation of organisms having strong properties of antagonism on various soilborne pathogens. The ability of AM fungi to control soilborne diseases is mainly related to their capacity to stimulate the establishment of rhizobacteria against the favorable environment of pathogen within the mycorrhizosphere prior to the root infection. Recent advancement in scientific research has provided more clear picture in understanding the mechanisms involved in AM fungi/rhizobacteria interactions. Herein, this chapter includes the mechanisms of the AM fungi-mediated biocontrol, interactions between AM-associated bacteria and AM fungus extraradical network, AM-associated bacteria and biocontrol activities and unfavorable zone to pathogen development: the mycorrhizosphere

IDEAS project - Private sector health data sharing study in Uttar Pradesh

The IDEAS project sought to improve the health and survival of mothers and babies through generating evidence to inform policy and practice. This data collection contains expanded field notes of face-to-face, semi-structured interviews conducted with 48 purposively selected key informants in Lucknow, Allahabad and Hardoi as part of a rapid assessment to determine private sector barriers and enablers associated with the sharing of maternal and newborn health data with the public sector. It also includes photographs of example health records, study tools, and associated documentation