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443 results on '"Sridevi, K."'

3. Secure Multi Factor Authentication in Banking Sector

Author

Sridevi, K., Majulatha, Mathi, Vidya, Mutakuduru, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Kumar, Amit, editor, Gunjan, Vinit Kumar, editor, Senatore, Sabrina, editor, and Hu, Yu-Chen, editor

Published
2025
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8. Evaluation of antidepressant properties of hydroalcoholic extract of Aegle marmelos fruit (Bael fruit) in Swiss albino mice: A pre-clinical study

Author

Shefeek SN, Sridevi K, Nagapati P Bhat, and Roopa P Nayak

Subjects
aegle marmelos fruit, anti-depressants, forced swimming test, tail suspension test, Medicine
Abstract

Introduction: Depression is a prevalent psychiatric disorder affecting 3.8% of the global population. The clinically available antidepressants can cause serious adverse effects. Aegle marmelos fruit has shown a wide range of pharmacological effects. The present study was carried out to elucidate the possible antidepressant activity of Aegle marmelos (Bael) fruit in Swiss albino mice. Objective: To evaluate the possible antidepressant activity of Aegle marmelos fruit extract in Swiss albino mice. Methods: A total of 30 (n=30) male Swiss albino mice weighing 25-35 g, divided into five groups of six animals each were used for the study. Aegle marmelos fruit powder was subjected to solvent extraction in a soxhlet apparatus. The antidepressant activity of the hydroalcoholic extract of Aegle marmelos fruit was evaluated by using two models –Forced Swimming Test (FST) and Tail Suspension Test (TST). Results: One-way ANOVA was used for multiple comparisons followed by a post hoc (Bonferroni test) for comparison between groups. Aegle marmelos fruit at the dose of 100mg/kg and 200 mg/kg significantly reduced the immobility time in both the tests (TST and FST) compared to the control. The reduction in duration of immobility at the dose of 200 mg/kg was comparable to the standard drug Imipramine. Conclusion: The Hydroalcoholic extract of Aegle marmelos has significant antidepressant activity in both the animal models of depression (FST and TST).

Published
2023
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14. Hierarchical Cluster-Based Model to Evaluate Accuracy Metrics Based on Cluster Efficiency

Author

Sridevi, K. N., Pinnapati, Surekha, Prakasha, S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Marín-Raventós, Gabriela, editor, and Mishra, Durgesh Kumar, editor

Published
2022
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15. Control Plane Efficiency by Load Adjustment in SDN

Author

Sridevi, K., Saifulla, M. A., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Zhang, Yu-Dong, editor, Senjyu, Tomonobu, editor, So-In, Chakchai, editor, and Joshi, Amit, editor

Published
2022
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18. Evaluation of Strength Characteristics on Black Cotton Soil–Stone Dust Mixtures Reinforced with Shredded Tyre Rubber

Author

Prasad, D. S. V., Kumar, K. Bharath, Sridevi, K., Vishalakshi, K., Raju, G. V. R. Prasada, Raju, K. Jyothi, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Patel, Satyajit, editor, Solanki, C. H., editor, Reddy, Krishna R., editor, and Shukla, Sanjay Kumar, editor

Published
2021
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21. Health systems performance for hypertension control using a cascade of care approach in South Africa, 2011-2017.

Author

Mariet Benade, Zandile Mchiza, Rafeya V Raquib, Sridevi K Prasad, Lily D Yan, Alana T Brennan, Justine Davies, Nikkil Sudharsanan, Jennifer Manne-Goehler, Matthew P Fox, Jacob Bor, Sydney B Rosen, and Andrew C Stokes

Subjects
Public aspects of medicine, RA1-1270
Abstract

Hypertension is a major contributor to global morbidity and mortality. In South Africa, the government has employed a whole systems approach to address the growing burden of non-communicable diseases. We used a novel incident care cascade approach to measure changes in the South African health system's ability to manage hypertension between 2011 and 2017. We used data from Waves 1-5 of the National Income Dynamics Study (NIDS) to estimate trends in the hypertension care cascade and unmet treatment need across four successive cohorts with incident hypertension. We used a negative binomial regression to identify factors that may predict higher rates of hypertension control, controlling for socio-demographic and healthcare factors. In 2011, 19.6% (95%CI 14.2, 26.2) of individuals with incident hypertension were diagnosed, 15.4% (95%CI 10.8, 21.4) were on treatment and 7.1% had controlled blood pressure. By 2017, the proportion of individuals with diagnosed incident hypertension had increased to 24.4% (95%CI 15.9, 35.4). Increases in treatment (23.3%, 95%CI 15.0, 34.3) and control (22.1%, 95%CI 14.1, 33.0) were also observed, translating to a decrease in unmet need for hypertension care from 92.9% in 2011 to 77.9% in 2017. Multivariable regression showed that participants with incident hypertension in 2017 were 3.01 (95%CI 1.77, 5.13) times more likely to have a controlled blood pressure compared to those in 2011. Our data show that while substantial improvements in the hypertension care cascade occurred between 2011 and 2017, a large burden of unmet need remains. The greatest losses in the incident hypertension care cascades came before diagnosis. Nevertheless, whole system programming will be needed to sufficiently address significant morbidity and mortality related to having an elevated blood pressure.

Published
2023
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27. A checklist to guide sensitivity analyses and replications of impact evaluations.

Author

Prasad, Sridevi K., Kastel, Fiona, Pande, Suvarna, Zhang, Chenxiao, and Glandon, Douglas M.

Subjects
SOCIAL science research, RESEARCH personnel, KEYWORD searching, SENSITIVITY analysis, EVALUATION methodology
Abstract

The transparency, reproducibility, and ethics movement has helped promote efforts to improve the rigour and validity of social science research. However, replication research, where the original data is used to check the validity and robustness of the estimations and recommendations in the original paper, has not been widely used as a tool in this field. This article outlines a checklist and standardised process that can guide replication researchers in creating and justifying their replication plans. To create the checklist, we reviewed 31 resources that were identified through unstructured keyword searches. We also reviewed and coded replication studies from 3ie's Replication Program which includes more than 20 3ie-funded replication papers and guidance on our replication process. There were five categories of potential validation tests and robustness checks that researchers could include. The checklist attributes range from testing specific assumptions for select impact evaluation methods to data transformations or dealing with heterogeneity. We hope that this checklist helps stimulate demand for replication research and promotes the use of replication research as a tool within transparency, reproducibility and ethics. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Machine learning predicts nucleosome binding modes of transcription factors

Author

K. C. Kishan, Sridevi K. Subramanya, Rui Li, and Feng Cui

Subjects
Machine learning, Nucleosome binding modes, Transcription factors, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding, dyad binding, groove binding, and gyre spanning. However, there are substantial experimental challenges in measuring nucleosome binding modes for thousands of TFs in different species. Results We present a computational prediction of the binding modes based on TF protein sequences. With a nested cross-validation procedure, our model outperforms several fine-tuned off-the-shelf machine learning (ML) methods in the multi-label classification task. Our binary classifier for the EB mode performs better than these ML methods with the area under precision-recall curve achieving 75%. The end preference of most TFs is consistent with low nucleosome occupancy around their binding site in GM12878 cells. The nucleosome occupancy data is used as an alternative dataset to confirm the superiority of our EB classifier. Conclusions We develop the first ML-based approach for efficient and comprehensive analysis of nucleosome binding modes of TFs.

Published
2021
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29. Rapid evidence assessment of the impacts of sewerage, drainage, and piped water chlorination in urban settings of low- and middle-income countries

Author

Sridevi K. Prasad, Charlotte Lane, and Douglas Glandon

Subjects
chlorination, drainage, impact, lmic, sewerage, wash, Environmental technology. Sanitary engineering, TD1-1066
Abstract

The aim of this review was to rapidly synthesize the available evidence regarding the expected impacts of piped water chlorination, drainage, and sewerage on individuals living in low- and middle-income urban settings. A systematic search was conducted in bibliographic databases and library catalogs. Impact evaluations that included a comparator and one of the three interventions of interest, attempted to control for confounding and selection bias, and took place in urban settings in a low- or middle-income country were considered. Outcomes related to health, well-being, economic growth, and the bacterial content of water were considered. A total of 1,483 articles were identified, with 18 included after final screening. Most studies were case-control and took place in Brazil, the Pacific Islands, or south-western Asia. Fifteen studies considered sewerage, five considered chlorination, and two studies considered drainage with some studies considering multiple interventions. All primary outcomes were related to health. When implemented alone, the sewerage and chlorination interventions were largely successful. The evidence regarding the effects of chlorination, drainage, and sewerage interventions is limited and generally has a high risk of bias. When properly implemented, sewerage and chlorination are likely to have positive health impacts. However, when implemented poorly, all three interventions can have negative health impacts. HIGHLIGHTS Most studies focused on sewerage interventions.; Chlorination and sewerage interventions were successful in reducing disease burden.; Contamination sources included lack of improved WaSH, sources in the nearby environment, and household hygiene practices.; Infrastructure maintenance issues affected the interventions’ impacts.; Proper household hygiene practices should be included for effective interventions.;

Published
2021
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33. Performance Analysis of LMS and Fractional LMS Algorithms for Smart Antenna System

Author

Sridevi, K., Jhansi Rani, A., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Panda, Ganapati, editor, Satapathy, Suresh Chandra, editor, Biswal, Birendra, editor, and Bansal, Ramesh, editor

Published
2019
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36. A Systematic Approach of Advanced Dilated Convolution Network for Speaker Identification

Author

Hema Kumar Pentapati and Sridevi K

Subjects
Electrical and Electronic Engineering, Engineering (miscellaneous)
Abstract

Over the years, the Speaker recognition area is facing various challenges in identifying the speakers accurately. Remarkable changes came into existence with the advent of deep learning algorithms. Deep learning made a remarkable impact on the speaker recognition approaches. This paper introduces a simple novel architectural approach to an advanced Dilated Convolution network. The novel idea is to induce the well-structured log-Melspectrum to the proposed dilated convolution neural network and reduce the number of layers to 11. The network utilizes the Global average pooling to accumulate the outputs from all layers to get the feature vector representation for classification. Only 13 coefficients are extracted per frame of each speech sample. This novel dilated convolution neural network exhibits an accuracy of 90.97%, Equal Error Rate(EER) of 3.75% and 207 Seconds training time outperforms the existing systems on the LibriSpeech corpus.

Published
2023
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37. Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

Author

Hema Kumar Pentapati and Sridevi K

Subjects
Computer Networks and Communications, Hardware and Architecture, Electrical and Electronic Engineering, Log-MelSpectrum, MFCC, Speaker Identification, excitation features, Convolution Neural Network(CNN), LP Residual, deep learning, Deep Neural Network
Abstract

There are various challenges in identifying the speakers accurately. The Extraction of discriminative features is a vital task for accurate identification in the speaker identification task. Nowadays, speaker identification is widely investigated using deep learning. The complex and noisy speech data affects the performance of Mel Frequency Cepstral Coefficients (MFCC); hence, MFCC fails to represent the speaker characteristics accurately. In this proposed work, a novel text-independent speaker identification system is developed to enhance the performance by fusion of Log-MelSpectrum and excitation features. The excitation information is obtained due to the vibration of vocal folds, and it is represented using Linear Prediction (LP) residual. The various types of features extracted from the excitation are residual phase, sharpness, Energy of Excitation (EoE), and Strength of Excitation (SoE). The extracted features were processed with the dilated convolution neural network (dilated CNN) to fulfill the identification task. The extensive evaluation showed that the fusion of excitation features gives better results than the existing methods. The accuracy reaches 94.12% for 11 complex classes and 91.34% for 80 speakers, and Equal Error Rate (EER) is reduced to 1.16% for the proposed model. The proposed model is tested with the Librispeech corpus using Matlab 2021b tool, outperforming the existing baseline models. The proposed model achieves an accuracy improvement of 1.34% compared to the baseline system.

Published
2023
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40. Fruit, vegetable, and legume intake, and cardiovascular disease and deaths in 18 countries (PURE): a prospective cohort study

Author

Diaz, R, Orlandini, A, Linetsky, B, Toscanelli, S, Casaccia, G, Cuneo, JM Maini, Rahman, O, Yusuf, R, Azad, AK, Rabbani, KA, Cherry, HM, Mannan, A, Hassan, I, Talukdar, AT, Tooheen, RB, Khan, MU, Sintaha, M, Choudhury, T, Haque, R, Parvin, S, Avezum, A, Oliveira, GB, Marcilio, CS, Mattos, AC, Teo, K, Yusuf, S, Dejesus, J, Agapay, D, Tongana, T, Solano, R, Kay, I, Trottier, S, Rimac, J, Elsheikh, W, Heldman, L, Ramezani, E, Dagenais, G, Poirier, P, Turbide, G, Auger, D, De Bluts, A LeBlanc, Proulx, MC, Cayer, M, Bonneville, N, Lear, S, Gasevic, D, Corber, E, de Jong, V, Vukmirovich, I, Wielgosz, A, Fodor, G, Pipe, A, Shane, A, Lanas, F, Seron, P, Martinez, S, Valdebenito, A, Oliveros, M, Wei, Li, Lisheng, Liu, Chunming, Chen, Xingyu, Wang, Wenhua, Zhao, Hongye, Zhang, Xuan, Jia, Bo, Hu, Yi, Sun, Jian, Bo, Xiuwen, Zhao, Xiaohong, Chang, Tao, Chen, Hui, Chen, Qing, Deng, Xiaoru, Cheng, Xinye, He, Jian, Li, Juan, Li, Xu, Liu, Bing, Ren, Wei, Wang, Yang, Wang, Jun, Yang, Yi, Zhai, Manlu, Zhu, Fanghong, Lu, Jianfang, Wu, Yindong, Li, Yan, Hou, Liangqing, Zhang, Baoxia, Guo, Xiaoyang, Liao, Shiying, Zhang, Rongwen, Bian, Xiuzhen, Tian, Dong, Li, Di, Chen, Jianguo, Wu, Yize, Xiao, Tianlu, Liu, Peng, Zhang, Changlin, Dong, Ning, Li, Xiaolan, Ma, Yuqing, Yang, Rensheng, Lei, Minfan, Fu, Jing, He, Yu, Liu, Xiaojie, Xing, Qiang, Zhou, Lopez-Jaramillo, P, Lopez, PA Camacho, Garcia, R, Jurado, LJA, Gómez-Arbeláez, D, Arguello, JF, Dueñas, R, Silva, S, Pradilla, LP, Ramirez, F, Molina, DI, Cure-Cure, C, Perez, M, Hernandez, E, Arcos, E, Fernandez, S, Narvaez, C, Paez, J, Sotomayor, A, Garcia, H, Sanchez, G, David, T, Rico, A, Mony, P, Vaz, M, Bharathi, A V, Swaminathan, S, Kurpad, K Shankar AV, Jayachitra, KG, Kumar, N, Hospital, HAL, Mohan, V, Deepa, M, Parthiban, K, Anitha, M, Hemavathy, S, Rahulashankiruthiyayan, T, Anitha, D, Sridevi, K, Gupta, R, Panwar, RB, Mohan, I, Rastogi, P, Rastogi, S, Bhargava, R, Kumar, R, Thakur, J S, Patro, B, Lakshmi, PVM, Mahajan, R, Chaudary, P, Kutty, V Raman, Vijayakumar, K, Ajayan, K, Rajasree, G, Renjini, AR, Deepu, A, Sandhya, B, Asha, S, Soumya, HS, Kelishadi, R, Bahonar, A, Mohammadifard, N, Heidari, H, Yusoff, K, Ismail, TST, Ng, KK, Devi, A, Nasir, NM, Yasin, MM, Miskan, M, Rahman, EA, Arsad, MKM, Ariffin, F, Razak, SA, Majid, FA, Bakar, NA, Yacob, MY, Zainon, N, Salleh, R, Ramli, MKA, Halim, NA, Norlizan, SR, Ghazali, NM, Arshad, MN, Razali, R, Ali, S, Othman, HR, Hafar, CWJCW, Pit, A, Danuri, N, Basir, F, Zahari, SNA, Abdullah, H, Arippin, MA, Zakaria, NA, Noorhassim, I, Hasni, MJ, Azmi, MT, Zaleha, MI, Hazdi, KY, Rizam, AR, Sazman, W, Azman, A, Khatib, R, Khammash, U, Khatib, A, Giacaman, R, Iqbal, R, Afridi, A, Khawaja, R, Raza, A, Kazmi, K, Zatonski, W, Szuba, A, Zatonska, K, Ilow, R, Ferus, M, Regulska-Ilow, B, Rózanska, D, Wolyniec, M, Alkamel, Ali, M, Kruger, M A, Voster, H H, Schutte, A E, Wentzel-Viljoen, E, Eloff, FC, de Ridder, H, Moss, H, Potgieter, J, Roux, AA, Watson, M, de Wet, G, Olckers, A, Jerling, JC, Pieters, M, Hoekstra, T, Puoane, T, Igumbor, E, Tsolekile, L, Sanders, D, Naidoo, P, Steyn, N, Peer, N, Mayosi, B, Rayner, B, Lambert, V, Levitt, N, Kolbe-Alexander, T, Ntyintyane, L, Hughes, G, Swart, R, Fourie, J, Muzigaba, M, Xapa, S, Gobile, N, Ndayi, K, Jwili, B, Ndibaza, K, Egbujie, B, Rosengren, A, Boström, K Bengtsson, Gustavsson, A, Andreasson, M, Snällman, M, Wirdemann, L, Oguz, A, Imeryuz, N, Altuntas, Y, Gulec, S, Temizhan, A, Karsidag, K, Calik, KBT, Akalin, AAK, Caklili, OT, Keskinler, MV, Erbakan, AN, Yusufali, AM, Almahmeed, W, Swidan, H, Darwish, EA, Hashemi, ARA, Al-Khaja, N, Muscat-Baron, JM, Ahmed, SH, Mamdouh, TM, Darwish, WM, Abdelmotagali, MHS, Awed, SA Omer, Movahedi, GA, Hussain, F, Al Shaibani, H, Gharabou, RIM, Youssef, DF, Nawati, AZS, Salah, ZAR Abu, Abdalla, RFE, Al Shuwaihi, SM, Al Omairi, MA, Cadigal, OD, Alejandrino, R.S., Chifamba, J, Gwaunza, L, Terera, G, Mahachi, C, Murambiwa, P, Machiweni, T, Mapanga, R, Miller, Victoria, Mente, Andrew, Dehghan, Mahshid, Rangarajan, Sumathy, Zhang, Xiaohe, Swaminathan, Sumathi, Dagenais, Gilles, Gupta, Rajeev, Mohan, Viswanathan, Lear, Scott, Bangdiwala, Shrikant I, Schutte, Aletta E, Wentzel-Viljoen, Edelweiss, Avezum, Alvaro, Altuntas, Yuksel, Yusoff, Khalid, Ismail, Noorhassim, Peer, Nasheeta, Chifamba, Jephat, Diaz, Rafael, Rahman, Omar, Mohammadifard, Noushin, Lana, Fernando, Zatonska, Katarzyna, Wielgosz, Andreas, Yusufali, Afzalhussein, Iqbal, Romaina, Lopez-Jaramillo, Patricio, Khatib, Rasha, Rosengren, Annika, Li, Wei, Liu, Jiankang, Liu, Xiaoyun, Yin, Lu, Teo, Koon, Anand, Sonia, and Yusuf, Salim

Published
2017
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41. Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study

Author

Diaz, R, Orlandini, A, Linetsky, B, Toscanelli, S, Casaccia, G, Cuneo, JM Maini, Rahman, O, Yusuf, R, Azad, AK, Rabbani, KA, Cherry, HM, Mannan, A, Hassan, I, Talukdar, AT, Tooheen, RB, Khan, MU, Sintaha, M, Choudhury, T, Haque, R, Parvin, S, Avezum, A, Oliveira, GB, Marcilio, CS, Mattos, AC, Teo, K, Yusuf, S, Dejesus, J, Agapay, D, Tongana, T, Solano, R, Kay, I, Trottier, S, Rimac, J, Elsheikh, W, Heldman, L, Ramezani, E, Dagenais, G, Poirier, P, Turbide, G, Auger, D, De Bluts, A LeBlanc, Proulx, MC, Cayer, M, Bonneville, N, Lear, S, Gasevic, D, Corber, E, de Jong, V, Vukmirovich, I, Wielgosz, A, Fodor, G, Pipe, A, Shane, A, Lanas, F, Seron, P, Martinez, S, Valdebenito, A, Oliveros, M, Wei, Li, Lisheng, Liu, Chunming, Chen, Xingyu, Wang, Wenhua, Zhao, Hongye, Zhang, Xuan, Jia, Bo, Hu, Yi, Sun, Jian, Bo, Xiuwen, Zhao, Xiaohong, Chang, Tao, Chen, Hui, Chen, Qing, Deng, Xiaoru, Cheng, Xinye, He, Jian, Li, Juan, Li, Xu, Liu, Bing, Ren, Wei, Wang, Yang, Wang, Jun, Yang, Yi, Zhai, Manlu, Zhu, Fanghong, Lu, Jianfang, Wu, Yindong, Li, Yan, Hou, Liangqing, Zhang, Baoxia, Guo, Xiaoyang, Liao, Shiying, Zhang, Rongwen, Bian, Xiuzhen, Tian, Dong, Li, Di, Chen, Jianguo, Wu, Yize, Xiao, Tianlu, Liu, Peng, Zhang, Changlin, Dong, Ning, Li, Xiaolan, Ma, Yuqing, Yang, Rensheng, Lei, Minfan, Fu, Jing, He, Yu, Liu, Xiaojie, Xing, Qiang, Zhou, Lopez-Jaramillo, P, Lopez, PA Camacho, Garcia, R, Jurado, LJA, Gómez-Arbeláez, D, Arguello, JF, Dueñas, R, Silva, S, Pradilla, LP, Ramirez, F, Molina, DI, Cure-Cure, C, Perez, M, Hernandez, E, Arcos, E, Fernandez, S, Narvaez, C, Paez, J, Sotomayor, A, Garcia, H, Sanchez, G, David, T, Rico, A, Mony, P, Vaz, M, Bharathi, A V, Swaminathan, S, Kurpad, K Shankar AV, Jayachitra, KG, Kumar, N, Hospital, HAL, Mohan, V, Deepa, M, Parthiban, K, Anitha, M, Hemavathy, S, Rahulashankiruthiyayan, T, Anitha, D, Sridevi, K, Gupta, R, Panwar, RB, Mohan, I, Rastogi, P, Rastogi, S, Bhargava, R, Kumar, R, Thakur, J S, Patro, B, Lakshmi, PVM, Mahajan, R, Chaudary, P, Kutty, V Raman, Vijayakumar, K, Ajayan, K, Rajasree, G, Renjini, AR, Deepu, A, Sandhya, B, Asha, S, Soumya, HS, Kelishadi, R, Bahonar, A, Mohammadifard, N, Heidari, H, Yusoff, K, Ismail, TST, Ng, KK, Devi, A, Nasir, NM, Yasin, MM, Miskan, M, Rahman, EA, Arsad, MKM, Ariffin, F, Razak, SA, Majid, FA, Bakar, NA, Yacob, MY, Zainon, N, Salleh, R, Ramli, MKA, Halim, NA, Norlizan, SR, Ghazali, NM, Arshad, MN, Razali, R, Ali, S, Othman, HR, Hafar, CWJCW, Pit, A, Danuri, N, Basir, F, Zahari, SNA, Abdullah, H, Arippin, MA, Zakaria, NA, Noorhassim, I, Hasni, MJ, Azmi, MT, Zaleha, MI, Hazdi, KY, Rizam, AR, Sazman, W, Azman, A, Khatib, R, Khammash, U, Khatib, A, Giacaman, R, Iqbal, R, Afridi, A, Khawaja, R, Raza, A, Kazmi, K, Zatonski, W, Szuba, A, Zatonska, K, Ilow, R, Ferus, M, Regulska-Ilow, B, Rózanska, D, Wolyniec, M, Alkamel, Ali, M, Kruger, M A, Voster, H H, Schutte, A E, Wentzel-Viljoen, E, Eloff, FC, de Ridder, H, Moss, H, Potgieter, J, Roux, AA, Watson, M, de Wet, G, Olckers, A, Jerling, JC, Pieters, M, Hoekstra, T, Puoane, T, Igumbor, E, Tsolekile, L, Sanders, D, Naidoo, P, Steyn, N, Peer, N, Mayosi, B, Rayner, B, Lambert, V, Levitt, N, Kolbe-Alexander, T, Ntyintyane, L, Hughes, G, Swart, R, Fourie, J, Muzigaba, M, Xapa, S, Gobile, N, Ndayi, K, Jwili, B, Ndibaza, K, Egbujie, B, Rosengren, A, Boström, K Bengtsson, Gustavsson, A, Andreasson, M, Snällman, M, Wirdemann, L, Oguz, A, Imeryuz, N, Altuntas, Y, Gulec, S, Temizhan, A, Karsidag, K, Calik, KBT, Akalin, AAK, Caklili, OT, Keskinler, MV, Erbakan, AN, Yusufali, AM, Almahmeed, W, Swidan, H, Darwish, EA, Hashemi, ARA, Al-Khaja, N, Muscat-Baron, JM, Ahmed, SH, Mamdouh, TM, Darwish, WM, Abdelmotagali, MHS, Awed, SA Omer, Movahedi, GA, Hussain, F, Al Shaibani, H, Gharabou, RIM, Youssef, DF, Nawati, AZS, Salah, ZAR Abu, Abdalla, RFE, Al Shuwaihi, SM, Al Omairi, MA, Cadigal, OD, Alejandrino, R.S., Chifamba, J, Gwaunza, L, Terera, G, Mahachi, C, Murambiwa, P, Machiweni, T, Mapanga, R, Dehghan, Mahshid, Mente, Andrew, Zhang, Xiaohe, Swaminathan, Sumathi, Li, Wei, Mohan, Viswanathan, Iqbal, Romaina, Kumar, Rajesh, Wentzel-Viljoen, Edelweiss, Rosengren, Annika, Amma, Leela Itty, Avezum, Alvaro, Chifamba, Jephat, Diaz, Rafael, Khatib, Rasha, Lear, Scott, Lopez-Jaramillo, Patricio, Liu, Xiaoyun, Gupta, Rajeev, Mohammadifard, Noushin, Gao, Nan, Oguz, Aytekin, Ramli, Anis Safura, Seron, Pamela, Sun, Yi, Szuba, Andrzej, Tsolekile, Lungiswa, Wielgosz, Andreas, Yusuf, Rita, Hussein Yusufali, Afzal, Teo, Koon K, Rangarajan, Sumathy, Dagenais, Gilles, Bangdiwala, Shrikant I, Islam, Shofiqul, Anand, Sonia S, and Yusuf, Salim

Published
2017
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42. Association of dietary nutrients with blood lipids and blood pressure in 18 countries: a cross-sectional analysis from the PURE study

Author

Yusuf, S, Rangarajan, S, Teo, K K, Chow, C K, O'Donnell, M, Mente, A, Leong, D, Smyth, A, Joseph, P, Merchant, A, Islam, S, Zhang, M, Hu, W, Ramasundarahettige, C, Wong, G, Bangdiwala, S, Dyal, L, Casanova, A, Dehghan, M, Lewis, G, Aliberti, A, Arshad, A, Reyes, A, Zaki, A, Lewis, B, Zhang, B, Agapay, D, Hari, D, Milazzo, E, Ramezani, E, Hussain, F, Shifaly, F, McAlpine, G, Kay, I, Lindeman, J, Rimac, J, Swallow, J, Heldman, L, Mushtaha, M(a), Mushtaha, M(o), Trottier, M, Riggi, M, Aoucheva, N, Kandy, N, Mackie, P, Solano, R, Chin, S, Ramacham, S, Shahrook, S, Trottier, S, Tongana, T, ElSheikh, W, Iyengar, Y, McQueen, M, Hall, K, Keys, J, Wang, X, Keneth, J, Devanath, A, Diaz, R, Orlandini, A, Linetsky, B, Toscanelli, S, Casaccia, G, Cuneo, JM Maini, Rahman, O, Yusuf, R, Azad, AK, Rabbani, KA, Cherry, HM, Mannan, A, Hassan, I, Talukdar, AT, Tooheen, RB, Khan, MU, Sintaha, M, Choudhury, T, Haque, R, Parvin, S, Avezum, A, Oliveira, GB, Marcilio, CS, Mattos, AC, Teo, K, Dejesus, J, Elsheikh, W, Dagenais, G, Poirier, P, Turbide, G, Auger, D, De Bluts, A LeBlanc, Proulx, MC, Cayer, M, Bonneville, N, Lear, S, Gasevic, D, Corber, E, de Jong, V, Vukmirovich, I, Wielgosz, A, Fodor, G, Pipe, A, Shane, A, Lanas, F, Seron, P, Martinez, S, Valdebenito, A, Oliveros, M, Wei, Li, Lisheng, Liu, Chunming, Chen, Xingyu, Wang, Wenhua, Zhao, Hongye, Zhang, Xuan, Jia, Bo, Hu, Yi, Sun, Jian, Bo, Xiuwen, Zhao, Xiaohong, Chang, Tao, Chen, Hui, Chen, Qing, Deng, Xiaoru, Cheng, Xinye, He, Jian, Li, Juan, Li, Xu, Liu, Bing, Ren, Wei, Wang, Yang, Wang, Jun, Yang, Yi, Zhai, Manlu, Zhu, Fanghong, Lu, Jianfang, Wu, Yindong, Li, Yan, Hou, Liangqing, Zhang, Baoxia, Guo, Xiaoyang, Liao, Shiying, Zhang, Rongwen, Bian, Xiuzhen, Tian, Dong, Li, Di, Chen, Jianguo, Wu, Yize, Xiao, Tianlu, Liu, Peng, Zhang, Changlin, Dong, Ning, Li, Xiaolan, Ma, Yuqing, Yang, Rensheng, Lei, Minfan, Fu, Jing, He, Yu, Liu, Xiaojie, Xing, Qiang, Zhou, Lopez-Jaramillo, P, Lopez, PA Camacho, Garcia, R, Jurado, LJA, Gómez-Arbeláez, D, Arguello, JF, Dueñas, R, Silva, S, Pradilla, LP, Ramirez, F, Molina, DI, Cure-Cure, C, Perez, M, Hernandez, E, Arcos, E, Fernandez, S, Narvaez, C, Paez, J, Sotomayor, A, Garcia, H, Sanchez, G, David, T, Rico, A, Mony, P, Vaz, M, Bharathi, A V, Swaminathan, S, Kurpad, K Shankar AV, Jayachitra, KG, Kumar, N, Hospital, HAL, Mohan, V, Deepa, M, Parthiban, K, Anitha, M, Hemavathy, S, Rahulashankiruthiyayan, T, Anitha, D, Sridevi, K, Gupta, R, Panwar, RB, Mohan, I, Rastogi, P, Rastogi, S, Bhargava, R, Kumar, R, Thakur, J S, Patro, B, Lakshmi, PVM, Mahajan, R, Chaudary, P, Kutty, V Raman, Vijayakumar, K, Ajayan, K, Rajasree, G, Renjini, AR, Deepu, A, Sandhya, B, Asha, S, Soumya, HS, Kelishadi, R, Bahonar, A, Mohammadifard, N, Heidari, H, Yusoff, K, Ismail, TST, Ng, KK, Devi, A, Nasir, NM, Yasin, MM, Miskan, M, Rahman, EA, Arsad, MKM, Ariffin, F, Razak, SA, Majid, FA, Bakar, NA, Yacob, MY, Zainon, N, Salleh, R, Ramli, MKA, Halim, NA, Norlizan, SR, Ghazali, NM, Arshad, MN, Razali, R, Ali, S, Othman, HR, Hafar, CWJCW, Pit, A, Danuri, N, Basir, F, Zahari, SNA, Abdullah, H, Arippin, MA, Zakaria, NA, Noorhassim, I, Hasni, MJ, Azmi, MT, Zaleha, MI, Hazdi, KY, Rizam, AR, Sazman, W, Azman, A, Khatib, R, Khammash, U, Khatib, A, Giacaman, R, Iqbal, R, Afridi, A, Khawaja, R, Raza, A, Kazmi, K, Dans, A, Co, HU, Sanchez, JT, Pudol, L, Zamora-Pudol, C, Palileo-Villanueva, LAM, Aquino, MR, Abaquin, C, Pudol, SL, Cabral, ML, Zatonski, W, Szuba, A, Zatonska, K, Ilow, R, Ferus, M, Regulska-Ilow, B, Rózanska, D, Wolyniec, M, AlHabib, KF, Hersi, A, Kashour, T, Alfaleh, H, Alshamiri, M, Altaradi, HB, Alnobani, O, Bafart, A, Alkamel, N, Ali, M, Abdulrahman, M, Nouri, R, Kruger, A, Voster, H H, Schutte, A E, Wentzel-Viljoen, E, Eloff, FC, de Ridder, H, Moss, H, Potgieter, J, Roux, AA, Watson, M, de Wet, G, Olckers, A, Jerling, JC, Pieters, M, Hoekstra, T, Puoane, T, Igumbor, E, Tsolekile, L, Sanders, D, Naidoo, P, Steyn, N, Peer, N, Mayosi, B, Rayner, B, Lambert, V, Levitt, N, Kolbe-Alexander, T, Ntyintyane, L, Hughes, G, Swart, R, Fourie, J, Muzigaba, M, Xapa, S, Gobile, N, Ndayi, K, Jwili, B, Ndibaza, K, Egbujie, B, Rosengren, A, Bengtsson Boström, K, Gustavsson, A, Andreasson, M, Snällman, M, Wirdemann, L, Yeates, K, Sleeth, J, Kilonzo, K, Oguz, A, Imeryuz, N, Altuntas, Y, Gulec, S, Temizhan, A, Karsidag, K, Calik, KBT, Akalin, AAK, Caklili, OT, Keskinler, MV, Erbakan, AN, Yusufali, AM, Almahmeed, W, Swidan, H, Darwish, EA, Hashemi, ARA, Al-Khaja, N, Muscat-Baron, JM, Ahmed, SH, Mamdouh, TM, Darwish, WM, Abdelmotagali, MHS, Awed, SA Omer, Movahedi, GA, Al Shaibani, H, Gharabou, RIM, Youssef, DF, Nawati, AZS, Salah, ZAR Abu, Abdalla, RFE, Al Shuwaihi, SM, Al Omairi, MA, Cadigal, OD, Alejandrino, R.S., Chifamba, J, Gwaunza, L, Terera, G, Mahachi, C, Murambiwa, P, Machiweni, T, Mapanga, R, Mente, Andrew, Dehghan, Mahshid, Rangarajan, Sumathy, McQueen, Matthew, Dagenais, Gilles, Wielgosz, Andreas, Lear, Scott, Li, Wei, Chen, Hui, Wang, Yang, Diaz, Rafael, Avezum, Alvaro, Lopez-Jaramillo, Patricio, Seron, Pamela, Kumar, Rajesh, Gupta, Rajeev, Mohan, Viswanathan, Swaminathan, Sumathi, Kutty, Raman, Zatonska, Katarzyna, Iqbal, Romaina, Yusuf, Rita, Mohammadifard, Noushin, Khatib, Rasha, Nasir, Nafiza Mat, Ismail, Noorhassim, Oguz, Aytekin, Rosengren, Annika, Yusufali, Afzalhussein, Wentzel-Viljoen, Edelweiss, Puoane, Thandi, Chifamba, Jephat, Teo, Koon, Anand, Sonia S, and Yusuf, Salim

Published
2017
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43. Availability and affordability of blood pressure-lowering medicines and the effect on blood pressure control in high-income, middle-income, and low-income countries: an analysis of the PURE study data

Author

Yusuf, S, Rangarajan, S, Teo, K K, Chow, C K, O'Donnell, M, Mente, A, Leong, D, Smyth, A, Joseph, P, Islam, S, Zhang, M, Hu, W, Ramasundarahettige, C, Wong, G, Dayal, L, Casanova, A, Dehghan, M, Lewis, G, Aliberti, A, Reyes, A, Zaki, A, Lewis, B, Zhang, B, Agapay, D, Hari, D, Milazzo, E, Ramezani, E, Hussain, F, Shifaly, F, Kay, I, Rimac, J, Swallow, J, Heldman, L, Mushtaha, M(a), Mushtaha, M(o), Trottier, M, Aoucheva, N, Kandy, N, Mackie, P, Solano, R, Chin, S, Ramacham, S, Shahrook, S, Trottier, S, Tongana, T, ElSheikh, W, Lindeman, J, McQueen, M, Hall, K, Keys, J, Wang, X, Keneth, J, Devanath, A, Diaz, R, Orlandini, A, Linetsky, B, Toscanelli, S, Casaccia, G, Maini Cuneo, JM, Rahman, O, Yusuf, R, Azad, AK, Rabbani, KA, Cherry, HM, Mannan, A, Hassan, I, Talukdar, AT, Tooheen, RB, Khan, MU, Sintaha, M, Choudhury, T, Haque, R, Parvin, S, Avezum, A, Oliveira, GB, Marcilio, CS, Mattos, AC, Teo, K, Dejesus, J, Elsheikh, W, Dagenais, G, Poirier, P, Turbide, G, Auger, D, De Bluts, A LeBlanc, Proulx, MC, Cayer, M, Bonneville, N, Lear, S, Gasevic, D, Corber, E, de Jong, V, Vukmirovich, I, Wielgosz, A, Fodor, G, Pipe, A, Shane, A, Lanas, F, Seron, P, Martinez, S, Valdebenito, A, Oliveros, M, Wei, Li, Lisheng, Liu, Chunming, Chen, Xingyu, Wang, Wenhua, Zhao, Hongye, Zhang, JiaXuan, Bo, Hu, Yi, Sun, Jian, Bo, Xiuwen, Zhao, Xiaohong, Chang, Tao, Chen, Hui, Chen, Qing, Deng, Xiaoru, Cheng, Xinye, He, Jian, Li, Juan, Li, Xu, Liu, Bing, Ren, Wei, Wang, Yang, Wang, Jun, Yang, Yi, Zhai, Manlu, Zhu, Fanghong, Lu, Jianfang, Wu, Yindong, Li, Yan, Hou, Liangqing, Zhang, Baoxia, Guo, Xiaoyang, Liao, Shiying, Zhang, BianRongwen, TianXiuzhen, Dong, Li, Di, Chen, Jianguo, Wu, Yize, Xiao, Tianlu, Liu, Peng, Zhang, Changlin, Dong, Ning, Li, Xiaolan, Ma, Yuqing, Yang, Rensheng, Lei, Minfan, Fu, Jing, He, Yu, Liu, Xiaojie, Xing, Qiang, Zhou, Lopez-Jaramillo, P, Lopez, PA Camacho, Garcia, R, Jurado, LJA, Gómez-Arbeláez, D, Arguello, JF, Dueñas, R, Silva, S, Pradilla, LP, Ramirez, F, Molina, DI, Cure-Cure, C, Perez, M, Hernandez, E, Arcos, E, Fernandez, S, Narvaez, C, Paez, J, Sotomayor, A, Garcia, H, Sanchez, G, David, T, Rico, A, Mony, P, Vaz, M, Bharathi, A V, Swaminathan, S, Shankar, K, Kurpad, AV, Jayachitra, KG, Kumar, N, Hospital, HAL, Mohan, V, Deepa, M, Parthiban, K, Anitha, M, Hemavathy, S, Rahulashankiruthiyayan, T, Anitha, D, Sridevi, K, Gupta, R, Panwar, RB, Mohan, I, Rastogi, P, Rastogi, S, Bhargava, R, Kumar, R, Thakur, J S, Patro, B, Lakshmi, PVM, Mahajan, R, Chaudary, P, Kutty, V Raman, Vijayakumar, K, Ajayan, K, Rajasree, G, Renjini, AR, Deepu, A, Sandhya, B, Asha, S, Soumya, HS, Kelishadi, R, Bahonar, A, Mohammadifard, N, Heidari, H, Yusoff, K, Ismail, TST, Ng, KK, Devi, A, Nasir, NM, Yasin, MM, Miskan, M, Rahman, EA, Arsad, MKM, Ariffin, F, Razak, SA, Majid, FA, Bakar, NA, Yacob, MY, Zainon, N, Salleh, R, Ramli, MKA, Halim, NA, Norlizan, SR, Ghazali, NM, Arshad, MN, Razali, R, Ali, S, Othman, HR, Hafar, CWJCW, Pit, A, Danuri, N, Basir, F, Zahari, SNA, Abdullah, H, Arippin, MA, Zakaria, NA, Noorhassim, I, Hasni, MJ, Azmi, MT, Zaleha, MI, Hazdi, KY, Rizam, AR, Sazman, W, Azman, A, Khatib, R, Khammash, U, Khatib, A, Giacaman, R, Iqbal, R, Afridi, A, Khawaja, R, Raza, A, Kazmi, K, Dans, A, Co, HU, Sanchez, JT, Pudol, L, Zamora-Pudol, C, Palileo-Villanueva, LAM, Aquino, MR, Abaquin, C, Pudol, SL, Cabral, ML, Zatonski, W, Szuba, A, Zatonska, K, Ilow#, R, Ferus, M, Regulska-Ilow, B, Różańska, D, Wolyniec, M, AlHabib, KF, Hersi, A, Kashour, T, Alfaleh, H, Alshamiri, M, Altaradi, HB, Alnobani, O, Bafart, A, Alkamel, N, Ali, M, Abdulrahman, M, Nouri, R, Kruger, A, Voster, H H, Schutte, A E, Wentzel-Viljoen, E, Eloff, FC, de Ridder, H, Moss, H, Potgieter, J, Roux, AA, Watson, M, de Wet, G, Olckers, A, Jerling, JC, Pieters, M, Hoekstra, T, Puoane, T, Igumbor, E, Tsolekile, L, Sanders, D, Naidoo, P, Steyn, N, Peer, N, Mayosi, B, Rayner, B, Lambert, V, Levitt, N, Kolbe-Alexander, T, Ntyintyane, L, Hughes, G, Swart, R, Fourie, J, Muzigaba, M, Xapa, S, Gobile, N, Ndayi, K, Jwili, B, Ndibaza, K, Egbujie, B, Rosengren, A, Boström, K Bengtsson, Lindblad, U, Langkilde, P, Gustavsson, A, Andreasson, M, Snällman, M, Wirdemann, L, Pettersson, K, Moberg, E, Yeates, K, Sleeth, J, Kilonzo, K, Oguz, A, Akalin, AAK, Calik, KBT, Imeryuz, N, Temizhan, A, Alphan, E, Gunes, E, Sur, H, Karsidag, K, Gulec, S, Altuntas, Y, Yusufali, AM, Almahmeed, W, Swidan, H, Darwish, EA, Hashemi, ARA, Al-Khaja, N, Muscat-Baron, JM, Ahmed, SH, Mamdouh, TM, Darwish, WM, Abdelmotagali, MHS, Awed, SA Omer, Movahedi, GA, Shaibani, H Al, Gharabou, RIM, Youssef, DF, Nawati, AZS, Salah, ZAR Abu, Abdalla, RFE, Shuwaihi, SM Al, Omairi, MA Al, Cadigal, OD, Alejandrino, R.S., Chifamba, J, Gwaunza, L, Terera, G, Mahachi, C, Murambiwa, P, Machiweni, T, Mapanga, R, Attaei, Marjan W, Khatib, Rasha, McKee, Martin, Lear, Scott, Dagenais, Gilles, Igumbor, Ehimario U, AlHabib, Khalid F, Kaur, Manmeet, Kruger, Lanthe, Teo, Koon, Lanas, Fernando, Yusoff, Khalid, Oguz, Aytekin, Gupta, Rajeev, Yusufali, Afzalhussein M, Bahonar, Ahmad, Kutty, Raman, Rosengren, Annika, Mohan, Viswanathan, Avezum, Alvaro, Yusuf, Rita, Szuba, Andrzej, Rangarajan, Sumathy, Chow, Clara, and Yusuf, Salim

Published
2017
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