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102 results on '"Mathur, Priyanka"'

2. Measurements of Impoverishing and Catastrophic Surgical Health Expenditures in Low- and Middle-Income Countries and Reduction Interventions in the Last 30 Years: A Systematic Review

Author

Klazura, Greg, Wong, Lye-Yeng, Ribeiro, Lucas Loiola Ponte Albuquerque, Kojo Anyomih, Theophilus Teddy, Ooi, Reuben Yih Khai, Berhane Fissha, Aemon, Alam, Syeda Fatema, Daudu, Davina, Nyalundja, Arsene Daniel, Beltrano, Joana, Patil, Poorvaprabha P., Wafford, Q. Eileen, Rapolti, Diana Ioana, Sullivan, Gwyneth A., Graf, Akua, Veras, Perry, Nico, Elsa, Sheth, Monica, Shing, Samuel R., Mathur, Priyanka, and Langer, Monica

Published
2024
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5. Cyber Security Threat, it’s Risks and Susceptibilities, in the Global Perspective

Author

Gupta, Radhika Mohan, Mathur, Priyanka, Nanda, Ruchi, 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, Rathore, Vijay Singh, editor, Sharma, Subhash Chander, editor, Tavares, Joao Manuel R.S., editor, Moreira, Catarina, editor, and Surendiran, B., editor

Published
2022
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7. Heuristic Expert Evaluation of e-Learning Application

Author

Mathur, Priyanka, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rathore, Vijay Singh, editor, Dey, Nilanjan, editor, Piuri, Vincenzo, editor, Babo, Rosalina, editor, Polkowski, Zdzislaw, editor, and Tavares, João Manuel R. S., editor

Published
2021
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23. Preliminary Studies on Optimization of Anti-Parkinson Drug Loaded Lipid Nanoparticles Enriched Hydrogel Formulations for Management of Parkinson’s Disease

Author

Alli Ramesh, A. Mamas Mamas, Kumar Deepak, Alasnag Mirvat, A. Quadros Vanessa, H.L. Purohit Abhishek, Salim Samina, Dey Abhijit, R. S. Santos Adair, Rani Vibha, U. Sneddon Lynne, Pantos Constantinos, Parwani Purvi, Bharti Sushma, A. Anastasiou Ioanna, Yunaiyama Daisuke, Khichar Satyendra, Tentolouris Anastasios, Nagakawa Yuichi, Leopoldo Harada Taiyo, Krishana Bohra Gopal, Mathur Priyanka, Tentolouris Nikolaos, B. Rosemberg Denis, Gorre Thirupathi, Mourouzis Iordanis, Eleftheriadou Ioanna, S. de Abreu Murilo, V. Costa Fabiano, Saito Kazuhiro, V. Kalueff Allan, Yamaguchi Hiroshi, Nagao Toshitaka, V. Rosa Luiz, and Swamy Kumara

Subjects
Drug, business.industry, media_common.quotation_subject, Biomedical Engineering, Medicine (miscellaneous), Medicine, Bioengineering, Pharmacology, business, Management of Parkinson's disease, media_common
Abstract

Introduction: Ropinirole (RP), is a selective dopamine agonist that is used alone or with other medications to treat the symptoms of Parkinson’s disease (PD). RP has low bioavailability of only about 50% due to the first-pass metabolism, and it requires frequent dosing during oral administration. Aim: The objective of the current research was to develop RP-loaded solid lipid nanoparticles (RP- SLNs), nanostructured lipid carriers (RP-NLCs), and their corresponding hydrogels (RP-SLN-C and RP-NLC-C) that could enhance RP therapeutic outcomes during PD treatment. Methods: RP nanoparticles were prepared by homogenization followed by probe sonication and optimized based on particle size, polydispersity index (PDI), zeta potential (ZP), % assay, % entrapment efficiency, and in vitro release studies. Optimized formulations were converted into hydrogel formulations using Carbopol 934 as a gelling polymer and optimized based on rheological and release characteristics. Optimized formulations were further evaluated using differential scanning calorimetry (DSC), powder X-ray diffractometry (PXRD), scanning electron microscopy (SEM), freeze-drying, and stability study at refrigerated and room temperatures. Results: The optimized RP-SLN formulation showed particle size and entrapment efficiency of 213.5±3.8 nm and 77.9±3.1% compared to 190.6±3.7 nm and 85.7±1.7% for optimized RP-NLC formulation. PXRD supplemented and confirmed DSC results, RP was entrapped in a molecularly dispersed state inside the core of the lipid nanocarrier. Furthermore, RP-loaded lipid nanocarriers revealed a spherical shape in SEM images. In vitro release studies demonstrated sustained release profiles for RP from SLNs, NLCs, and their hydrogels over 24 h. Optimized SLN, NLC, and nanocarrier- loaded hydrogel formulations were stable over three months at 4ºC and 25ºC storage conditions. Conclusion: Overall, the results demonstrated that lipid nanocarriers and their corresponding hydrogel formulations can be considered as a topical drug delivery vehicle for RP during the treatment of PD.

Published
2021
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38. Inter-Patient Atrial Flutter Classification Using FFT-Based Features and a Low-Variance Stacking Classifier.

Author

Besler, Emre, Mathur, Priyanka, Gay, Hawkins, Passman, Rod, and Sahakian, Alan

Subjects
*ATRIAL flutter, *SUPPORT vector machines, *K-nearest neighbor classification, *RANDOM forest algorithms, *CATHETER ablation, *MACHINE learning
Abstract

Objective: Atrial Flutter (AFL) is a supraventricular tachyarrhythmia typically arising from a macroreentry circuit that can have variable atrial anatomy. It is often treated by catheter ablation, the success of which depends upon the correct determination of the electroanatomic circuit, generally through invasive electrophysiological (EP) study. We hypothesized that machine learning (ML) methods applied to the diagnostic 12-lead surface electrocardiogram (ECG) could determine the specific circuit prior to any invasive EP study. Methods: The 12-lead ECGs were reduced to eight independent leads: I, II, V1 – V6. Through an algorithm using ventricular complex cancellation methods, windows of atrial activity in the ECG were uncovered and spectra were generated. Three ML classifier approaches were applied: Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbors (KNN), and their outputs combined using soft voting. Results: Ten-second surface ECGs taken from 419 AFL patients prior to invasive EP study and ablation were analyzed retrospectively. Of the 419 patients, 285 had typical cavotricuspid isthmus (CTI)-dependent AFL, 41 had atypical right-atrial AFL and 93 had left-atrial AFL, as determined during the subsequent EP study. Lead V5 was found to be most useful giving a test accuracy of 98% and f1 score of 0.97. Conclusion: We conclude that ML methods have the potential to automatically determine the AFL macroreentry circuit from the surface ECG. Significance: The AFL classification method presented in this investigation achieves 95+% accuracy on an unbalanced inter-patient dataset which has important clinical applications. [ABSTRACT FROM AUTHOR]

Published
2022
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