ASSESMENT OF QUALITY OF LIFE AND OXIDATIVE STRESS IN TUBERCULOSIS PATIENTS VISITING DIRECTLY OBSERVED TREATMENT SHORT COURSE CENTRES OF WARANGAL

ABSTRACTObjectives: Tuberculosis (TB) is a disease associated with a wide range of respiratory symptoms. It remains a major public health problem worldwide.In TB, oxidative stress is a result of tissue inflammation, poor dietary intake of micronutrients due to illness, and free radical burst from activatedmacrophages. In recent years, efforts have been dedicated for assessing the health-related quality of life (HRQoL) in TB patients. The objectives of thestudy were to evaluate the impairment of HRQoL in TB patients using by DR-12 questionnaire and to estimate oxidative stress parameters such asmalondialdehyde (MDA), glutathione (GSH), vitamins A, and C in TB patients.Methods: A total of 142 patients meeting the study criteria were recruited in the study to evaluate HRQOL. The patients were administered withDR-12 questionnaire at 0 week, 4 weeks and at the end of intensive phase of the treatment. A paired t-test was applied and a p<0.05 was consideredas significant. 40 patients meeting the study criteria were recruited for assessment of oxidative stress parameters. The blood samples were assessedfor the concentration of MDA, GSH, vitamin A, and vitamin C using suitable methods.Results: A significantly higher HRQOL scores were observed at the end of intensive phase of the treatment for both pulmonary and extrapulmonaryTB patients. There was a significant improvement in their QOL (p<0.05). An increased oxidative stress was obtained in plasma of TB patients ascompared to normal healthy controls. There was a significant increase in the MDA levels of TB patients (7 times greater than control) when comparedto normal population. There was a double decrease in GSH and vitamin A concentrations in TB cases compared with controls. The plasma levels ofvitamin C in TB cases obtained thrice lesser in TB cases than the control population.Conclusion: The study showed that in TB patients free radical activity is quite high and antioxidant levels are low. A suitable antioxidant therapy mayimprove QoL and prove beneficial supplementation for fast recovery.Keywords: Tuberculosis, Health-related quality of life, Directly observed treatment short course, DR-12 score, Antioxidants, Free radicals

ASSESSMENT OF PLACENTAL OXIDATIVE STRESS PARAMETERS IN PREECLAMPTIC AND NORMAL PREGNANT WOMEN

Objective: Oxidative stress occurs when cellular levels of reactive oxygen species exceed antioxidant capabilities and has been implicated in thepathogenesis of pre-eclampsia. In this study, we examined the tissue levels of endogenous antioxidant enzymes or proteins (superoxide dismutase[SOD], glutathione peroxidise, reduced glutathione, catalase, and thioredoxin) and the levels of lipid peroxides, protein carbonyls, hydrogen peroxide,and nitrosative biomarkers in the placental samples from normal and pre-eclamptic pregnancies.Results: Pre-eclamptic tissue homogenates demonstrated significantly increased levels of lipid peroxidation (21.61±0.18 vs. 5.695±0.46) anda trended increase in protein carbonyls (245.95±4.05 vs. 203.48±3.65) concentration when compared to controls. The levels and activities of theantioxidant proteins; SOD (365.2±2.915 vs. 205.6±3.76), thioredoxin (100.64±3.38 vs. 80.89±3.37), glutathione peroxidase (340.88±6.16 vs.164.46±3.03), catalase (5.26±0.02 vs. 4.62±0.11), and reduced glutathione (46.99±0.508 vs. 28.19±0.178) were all found to be significantly reducedwhen comparing pre-eclamptic placental tissue homogenates to gestational age matched control placentae from non pre-eclamptic pregnancies.Conclusion: The results of this study demonstrate a decreased enzymatic antioxidant capacity and increased oxidation in placental tissue from preeclampticwomen,whichmaycontributetothe pathogenesis of this complexdisorder.Keywords: Oxidative stress, Reactive oxygen species, Superoxide dismutase, Glutathione peroxidise, Reduced glutathione, Catalase, Thioredoxin,Lipid peroxides and protein carbonyls

Hybrid Approach for Prediction of Cardiovascular Disease Using Class Association Rules and MLP

:  In data mining classification techniques are used to predict group membership for data instances. These techniques are capable of processing a wider variety of data and the output can be easily interpreted. The aim of any classification algorithm is the design and conception of a standard model with reference to the given input. The model thus generated may be deployed to classify new examples or enable a better comprehension of available data.  Medical data classification is the process of transforming descriptions of medical diagnoses and procedures used to find hidden information. Two experiments are performed to identify the prediction accuracy of Cardiovascular Disease (CVD).A hybrid approach for classification is proposed in this paper by combining the results of the associate classifier and artificial neural networks (MLP).  The first experiment is performed using associative classifier to identify the key attributes which contribute more towards the decision by taking the 13 independent attributes as input. Subsequently classification using Multi Layer Perceptrons (MLP) also performed to generate the accuracy of prediction using all attributes. In the second experiment, identified key attributes using associative classifier are used as inputs for the feed forward neural networks for predicting the presence or absence of CVD

A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction

Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understanding and knowledge discovery, recent works in this domain have largely focused on binary classification of a text segment as causal or non-causal. In this regard, we perform a thorough analysis of three sequence tagging models for causal knowledge extraction and compare it with a span based approach to causality extraction. Our experiments show that embeddings from pre-trained language models (e.g. BERT) provide a significant performance boost on this task compared to previous state-of-the-art models with complex architectures. We observe that span based models perform better than simple sequence tagging models based on BERT across all 4 data sets from diverse domains with different types of cause-effect phrases

Improving Neural Ranking Models with Traditional IR Methods

Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions. Nevertheless, they are computationally expensive to create, and require a great deal of labeled data for specialized corpora. In this paper, we explore a low resource alternative which is a bag-of-embedding model for document retrieval and find that it is competitive with large transformer models fine tuned on information retrieval tasks. Our results show that a simple combination of TF-IDF, a traditional keyword matching method, with a shallow embedding model provides a low cost path to compete well with the performance of complex neural ranking models on 3 datasets. Furthermore, adding TF-IDF measures improves the performance of large-scale fine tuned models on these tasks.Comment: Short paper, 4 page

Performance of BVBlue Rapid Test in Detecting Bacterial Vaginosis among Women in Mysore, India

Bacterial vaginosis (BV) is the most common cause of abnormal vaginal discharge in reproductive age women. It is associated with increased susceptibility to HIV/STI and adverse birth outcomes. Diagnosis of BV in resource-poor settings like India is challenging. With little laboratory infrastructure there is a need for objective point-of-care diagnostic tests. Vaginal swabs were collected from women 18 years and older, with a vaginal pH \u3e 4.5 attending a reproductive health clinic. BV was diagnosed with Amsel’s criteria, Nugent scores, and the OSOM BVBlue test. Study personnel were blinded to test results. There were 347 participants enrolled between August 2009 and January 2010. BV prevalence was 45.1% (95% confidence interval (CI): 41.5%–52.8%) according to Nugent score. When compared with Nugent score, the sensitivity, specificity, positive predictive value, negative predictive value for Amsel’s criteria and BVBlue were 61.9%, 88.3%, 81.5%, 73.7% and 38.1%, 92.7%, 82.1%, 63.9%, respectively. Combined with a “whiff” test, the performance of BVBlue increased sensitivity to 64.4% and negative predictive value to 73.8%. Despite the good specificity, poor sensitivity limits the usefulness of the BVBlue as a screening test in this population. There is a need to examine the usefulness of this test in other Indian populations

Identification of triple negative breast cancer genes using rough set based feature selection algorithm & ensemble classifier

In recent decades, microarray datasets have played an important role in triple negative breast cancer (TNBC) detection. Microarray data classification is a challenging process due to the presence of numerous redundant and irrelevant features. Therefore, feature selection becomes irreplaceable in this research field that eliminates non-required feature vectors from the system. The selection of an optimal number of features significantly reduces the NP hard problem, so a rough set-based feature selection algorithm is used in this manuscript for selecting the optimal feature values. Initially, the datasets related to TNBC are acquired from gene expression omnibuses like GSE45827, GSE76275, GSE65194, GSE3744, GSE21653, and GSE7904. Then, a robust multi-array average technique is used for eliminating the outlier samples of TNBC/non-TNBC which helps enhancing classification performance. Further, the pre-processed microarray data are fed to a rough set theory for optimal gene selection, and then the selected genes are given as the inputs to the ensemble classification technique for classifying low-risk genes (non-TNBC) and high-risk genes (TNBC). The experimental evaluation showed that the ensemble-based rough set model obtained a mean accuracy of 97.24%, which superior related to other comparative machine learning techniques.Web of Science12art. no. 5

Simulation of attacks for security in wireless sensor network

The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node?s software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work.This work has been funded by the Spanish MICINN under the TEC2011-28666-C04-02 and TEC2014-58036-C4-3-R project