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7. Optical analysis of RE3+ (RE = Eu,Tb):MgLa2V2O9 nano‐phosphors.

Author

Hajira, Shaik, Vijitha, Jillela Santhosh, Dhoble, Sanjay Janrao, Raju, Borelli Deva Prasad, and Reddy, Busireddy Sudhakar

Abstract

Red and green rare‐earth ion (RE3+) (RE = Eu, Tb):MgLa2V2O9 micro‐powder phosphors were produced utilizing a standard solid‐state chemical process. The X‐ray diffraction examination performed on the phosphors showed that they were crystalline and had a monoclinic structure. The particles grouped together, as shown in the scanning electron microscopy (SEM) images. Powder phosphors were examined using a variety of spectroscopic techniques, including photoluminescence (PL), Fourier‐transform infrared, and energy dispersive X‐ray spectroscopy. Brilliant red emission at 615 nm (5D0 → 7F2) having an excitation wavelength (λexci) of 396 nm (7F0 → 5L6) and green emission at 545 nm (5D4 → 7F5) having an λexci = 316 nm (5D4 → 7F2) have both been seen in the emission spectra of Tb3+:MgLa2V2O9 nano‐phosphors. The emission mechanism that is raised in Eu3+:MgLa2V2O9 and Tb3+:MgLa2V2O9 powder phosphors has been explained in an energy level diagram. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Global Impact of the COVID-19 Pandemic on Stroke Volumes and Cerebrovascular Events: A 1-Year Follow-up

Author

Society of Vascular and Interventional Neurology (US), Nguyen, Thanh N., Qureshi, Muhammad M., Klein, Piers, Yamagami, Hiroshi, Mikulik, Robert, Czlonkowska, Anna, Abdalkader, Mohamad, Sedova, Petra, Sathya, Anvitha, Lo, Hannah C., Mansour, Ossama Yassin, Vanguru, Husitha Reddy, Lesaine, Emilie, Tsivgoulis, Georgios, Loochtan, Aaron I., Demeestere, Jelle, Uchino, Ken, Inoa, Violiza, Goyal, Nitin, Charidimou, Andreas, Siegler, James E., Yaghi, Shadi, Aguiar de Sousa, Diana, Mohammaden, Mahmoud H., Haussen, Diogo C., Kristoffersen, Espen Saxhaug, Lereis, Virginia Pujol, Scollo, Sergio Daniel, Campbell, Bruce C. V., Ma, Alice, Thomas, James Orton, Parsons, Mark W., Singhal, Shaloo, Slater, Lee-Anne, Tomazini Martins, Rodrigo, Enzinger, Chris, Gattringer, Thomas, Rahman, Aminur, Bonnet, Thomas, Ligot, Noemie, De Raedt, Sylvie, Lemmens, Robin, Vanacker, Peter, Vandervorst, Fenne, Conforto, Adriana Bastos, Hidalgo, Raquel C. T., Neves, Luciana de Oliveira, Martins, Rodrigo Targa, Mora Cuervo, Daissy Liliana, Rebello, Leticia C., Santiago, Igor Bessa, Silva, Isabelle Lameirinhas da, Sakelarova, Teodora, Kalpachki, Rosen, Alexiev, Filip, Catanese, Luciana, Cora, Elena Adela, Goyal, Mayank, Hill, Michael D., Kelly, Michael E., Khosravani, Houman, Lavoie, Pascale, Peeling, Lissa, Pikula, Aleksandra, Rivera, Rodrigo, Chen, Hui-Sheng, Chen, Yimin, Huo, Xiaochuan, Miao, Zhongrong, Yang, Shuiquan, Bedekovic, Marina Roje, Bralic, Marina, Budincevic, Hrvoje, Corredor-Quintero, Ángel Basilio, Lara-Sarabia, Osvaldo E., Cabal, Martin, Tenora, Dusan, Fibrich, Petr, Herzig, Roman, Hlaváčová, Helena, Hrabanovska, Emanuela, Hlinovsky, David, Jurak, Lubomir, Kadlcikova, Jana, Karpowicz, Igor, Klecka, Lukas, Kovar, Martin, Lauer, David, Neumann, Jiri, Palouskova, Hana, Reiser, Martin, Rekova, Petra, Rohan, Vladimir, Skoda, Ondrej, Škorňa, Miroslav, Sobotková, Lenka, Sramek, Martin, Zakova, Lenka, Christensen, Hanne, Drenck, Nicolas, Iversen, Helle Klingenberg, Truelsen, Thomas Clement, Wienecke, Troels, Sobh, Khalid, Ylikotila, Pauli, Alpay, Kemal, Strbian, Daniel, Bernady, Patricia, Casenave, Philippe, Dan, Maria, Faucheux, Jean-Marc, Gentric, Jean-Christophe, Magro, Elsa, Sabben, Candice, Reiner, Peggy, Rouanet, Francois, Bohmann, Ferdinand O., Boskamp, Stefan, Mbroh, Joshua, Nagel, Simon, Nolte, Christian H, Ringleb, Peter A., Rosenkranz, Michael, Poli, Sven, Thomalla, Götz, Karapanayiotides, Theodoros, Koutroulou, Ioanna, Kargiotis, Odysseas, Palaiodimou, Lina, Barrientos Guerra, José Domingo, Huded, Vikram, Menon, Bindu, Nagendra, Shashank, Prajapati, Chintan, Sylaja, P. N., Krishna Pramana, Nyoman Angga, Sani, Achmad Firdaus, Ghoreishi, Abdoreza, Farhoudi, Mehdi, Hokmabadi, Elyar Sadeghi, Raya, Tariq Abu, Kalmanovich, Shani Avnery, Ronen, Levite, Sabetay, Sergiu Ionut, Acampa, Maurizio, Adami, Alessandro, Castellan, Lucio, Longoni, Marco, Ornello, Raffaele, Renieri, Leonardo, Bigliani, Claudia Rolla, Romoli, Michele, Sacco, Simona, Salmaggi, Andrea, Sangalli, Davide, Zini, Andrea, Doijiri, Ryosuke, Fukuda, Hiroki, Fujinaka, Toshiyuki, Fujita, Kyohei, Imamura, Hirotoshi, Sakai, Nobuyuki, Kanamaru, Takuya, Kimura, Naoto, Kono, Ryuhei, Miyake, Kosuke, Sakaguchi, Manabu, Sakai, Kenichiro, Sonoda, Kazutaka, Todo, Kenichi, Miyashita, Fumio, Tokuda, Naoki, Matsumaru, Yuji, Matsumoto, Shoji, Ohara, Nobuyuki, Shindo, Seigo, Takenobu, Yohei, Yoshimoto, Takeshi, Toyoda, Kazunori, Uwatoko, Takeshi, Yagita, Yoshiki, Yamada, Takehiro, Yamamoto, Nobuaki, Yamamoto, Ryoo, Yazawa, Yukako, Sugiura, Yuri, Waweru, Peter Kuria, Baek, Jang-Hyun, Lee, Si Baek, Seo, Kwon-Duk, Sohn, Sung-Il, Arsovska, Anita Ante, Chan, Yong Chieh, Wan Zaidi, Wan Asyraf, Jaafar, Ainul Syahrilfazli, Gongora-Rivera, Fernando, Martinez-Marino, Manuel, Infante-Valenzuela, Adrian, Groppa, Stanislav, Leahu, Pavel, Coutinho, Jonathan M, Rinkel, Leon A., Dippel, Diederik W. J., Dam-Nolen, Dianne H. K. van, Ranta, Annemarei, Wu, Teddy Y., Adebayo, Tajudeen Temitayo, Bello, Abiodun H., Nwazor, Ernest Okwundu, Sunmonu, Taofiki Ajao, Wahab, Kolawole Wasiu, Ronning, Ole Morten, Sandset, Else Charlotte, Al Hashmi, Amal M., Ahmad, Saima, Rashid, Umair, Rodriguez-Kadota, Liliana, Vences, Miguel Ángel, Yalung, Patrick Matic, Hao Dy, Jon Stewart, Pineda-Franks, Maria Carissa, Co, Christian Oliver, Brola, Waldemar, Debiec, Aleksander, Dorobek, Malgorzata, Karlinski, Michal Adam, Labuz-Roszak, Beata M., Lasek-Bal, Anetta, Sienkiewicz-Jarosz, Halina, Staszewski, Jacek, Sobolewski, Piotr, Wiacek, Marcin, Zielinska-Turek, Justyna, Araujo, Andre Pinho, Rocha, Mariana, Castro, Pedro, Cruz, Vitor Tedim, Ferreira, Paulo Venancio, Ferreira, Patricia, Nunes, Ana Paiva, Fonseca, Luisa, Marto, João Pedro, Pinho E Melo, Teresa, Rodrigues, Miguel, Silva, M. Luis, Dimitriade, Adela, Falup-Pecurariu, Cristian, Hamid, May Adel, Venketasubramanian, Narayanaswamy, Krastev, Georgi, Mako, Miroslav, Ayo-Martin, Oscar, Hernández-Fernández, Francisco, Blasco, Jordi, Rodríguez-Vázquez, Alejandro, Cruz-Culebras, Antonio, Moniche, Francisco, Montaner, Joan, Pérez-Sánchez, Soledad, García Sánchez, María Jesús, Guillán Rodríguez, Marta, Jood, Katarina, Nordanstig, Annika, Mazya, Michael V., Moreira, Tiago T. P., Bernava, Gianmarco, Beyeler, Morin, Bolognese, Manuel, Carrera, Emmanuel, Dobrocky, Tomas, Karwacki, Grzegorz Marek, Keller, Emanuela, Hsieh, Chang Yang, Boonyakarnkul, Surawan, Churojana, Anchalee, Aykac, Ozlem, Ozdemir, Atilla Ã-Zcan, Bajrami, Arsida, Senadim, Songul, Hussain, Syed Irteza, John, Seby, Banerjee, Soma, Kwan, Joseph, Krishnan, Kailash, Lenthall, Robert, Matthews, Ashok, Wong, Ken, Zhang, Liqun, Altschul, Dorothea, Asif, Kaiz S., Bahiru, Zeelalem, Below, Kristine, Biller, José, Ruland, Sean, Chaudry, Saqib A., Chen, Michael, Chebl, Alex, Cibulka, Jackie, Cistrunk, Leon, Clark, Judith, Colasurdo, Marco, Czap, Alexandra, de Havenon, Adam, D'Amato, Salvatore, Dharmadhikari, Sushrut, Grimmett, Kasey B., Dmytriw, Adam A., Etherton, Mark R., Ezepue, Chizoba, Farooqui, Mudassir, Feske, Steven K., Fink, Lauren, Gasimova, Ulviyya, Guzik, Amy K., Hakemi, Maryam, Hovingh, Majesta, Khan, Muhib, Jillela, Dinesh, Kan, Peter T., Khatri, Rakesh, Khawaja, Ayaz M., Khoury, Naim N., Kiley, Nicole L., Kim, Benny S., Kolikonda, Murali K., Kuhn, Anna Luisa, Lara, Stephanie, Linares, Guillermo, Linfante, Italo, Lukovits, Timothy G., Lycan, Sarah, Male, Shailesh S., Maali, Laith, Mancin, John, Masoud, Hesham, Mohamed, Ghada A., Monteiro, Andre, Nahab, Fadi, Nalleballe, Krishna, Ortega-Gutiérrez, Santiago, Puri, Ajit S, Radaideh, Yazan, Rahangdale, Rahul H., Rai, Ansaar, Ramakrishnan, Pankajavalli, Reddy, Aravind B., Rojas-Soto, Diana M., Romero, José Rafael, Rost, Natalia S., Rothstein, Aaron, Omran, Setareh Salehi, Sheth, Sunil A., Siddiqui, Adnan H., Starosciak, Amy K., Tarlov, Nicholas E., Taylor, Robert A., Wang, Michael J., Wolfe, Jared, Wong, Ka-Ho, Le, Huynh Vu, Nguyen, Quy Viet, Pham, Thong Nhu, Nguyen, Trung Thanh, Phan, Hoang Thi, Ton, Mai Duy, Fischer, Urs, Michel, Patrik, Strambo, Davide, Martins, Sheila O., Zaidat, Osama O., Nogueira, Raul G., Society of Vascular and Interventional Neurology (US), Nguyen, Thanh N., Qureshi, Muhammad M., Klein, Piers, Yamagami, Hiroshi, Mikulik, Robert, Czlonkowska, Anna, Abdalkader, Mohamad, Sedova, Petra, Sathya, Anvitha, Lo, Hannah C., Mansour, Ossama Yassin, Vanguru, Husitha Reddy, Lesaine, Emilie, Tsivgoulis, Georgios, Loochtan, Aaron I., Demeestere, Jelle, Uchino, Ken, Inoa, Violiza, Goyal, Nitin, Charidimou, Andreas, Siegler, James E., Yaghi, Shadi, Aguiar de Sousa, Diana, Mohammaden, Mahmoud H., Haussen, Diogo C., Kristoffersen, Espen Saxhaug, Lereis, Virginia Pujol, Scollo, Sergio Daniel, Campbell, Bruce C. V., Ma, Alice, Thomas, James Orton, Parsons, Mark W., Singhal, Shaloo, Slater, Lee-Anne, Tomazini Martins, Rodrigo, Enzinger, Chris, Gattringer, Thomas, Rahman, Aminur, Bonnet, Thomas, Ligot, Noemie, De Raedt, Sylvie, Lemmens, Robin, Vanacker, Peter, Vandervorst, Fenne, Conforto, Adriana Bastos, Hidalgo, Raquel C. T., Neves, Luciana de Oliveira, Martins, Rodrigo Targa, Mora Cuervo, Daissy Liliana, Rebello, Leticia C., Santiago, Igor Bessa, Silva, Isabelle Lameirinhas da, Sakelarova, Teodora, Kalpachki, Rosen, Alexiev, Filip, Catanese, Luciana, Cora, Elena Adela, Goyal, Mayank, Hill, Michael D., Kelly, Michael E., Khosravani, Houman, Lavoie, Pascale, Peeling, Lissa, Pikula, Aleksandra, Rivera, Rodrigo, Chen, Hui-Sheng, Chen, Yimin, Huo, Xiaochuan, Miao, Zhongrong, Yang, Shuiquan, Bedekovic, Marina Roje, Bralic, Marina, Budincevic, Hrvoje, Corredor-Quintero, Ángel Basilio, Lara-Sarabia, Osvaldo E., Cabal, Martin, Tenora, Dusan, Fibrich, Petr, Herzig, Roman, Hlaváčová, Helena, Hrabanovska, Emanuela, Hlinovsky, David, Jurak, Lubomir, Kadlcikova, Jana, Karpowicz, Igor, Klecka, Lukas, Kovar, Martin, Lauer, David, Neumann, Jiri, Palouskova, Hana, Reiser, Martin, Rekova, Petra, Rohan, Vladimir, Skoda, Ondrej, Škorňa, Miroslav, Sobotková, Lenka, Sramek, Martin, Zakova, Lenka, Christensen, Hanne, Drenck, Nicolas, Iversen, Helle Klingenberg, Truelsen, Thomas Clement, Wienecke, Troels, Sobh, Khalid, Ylikotila, Pauli, Alpay, Kemal, Strbian, Daniel, Bernady, Patricia, Casenave, Philippe, Dan, Maria, Faucheux, Jean-Marc, Gentric, Jean-Christophe, Magro, Elsa, Sabben, Candice, Reiner, Peggy, Rouanet, Francois, Bohmann, Ferdinand O., Boskamp, Stefan, Mbroh, Joshua, Nagel, Simon, Nolte, Christian H, Ringleb, Peter A., Rosenkranz, Michael, Poli, Sven, Thomalla, Götz, Karapanayiotides, Theodoros, Koutroulou, Ioanna, Kargiotis, Odysseas, Palaiodimou, Lina, Barrientos Guerra, José Domingo, Huded, Vikram, Menon, Bindu, Nagendra, Shashank, Prajapati, Chintan, Sylaja, P. N., Krishna Pramana, Nyoman Angga, Sani, Achmad Firdaus, Ghoreishi, Abdoreza, Farhoudi, Mehdi, Hokmabadi, Elyar Sadeghi, Raya, Tariq Abu, Kalmanovich, Shani Avnery, Ronen, Levite, Sabetay, Sergiu Ionut, Acampa, Maurizio, Adami, Alessandro, Castellan, Lucio, Longoni, Marco, Ornello, Raffaele, Renieri, Leonardo, Bigliani, Claudia Rolla, Romoli, Michele, Sacco, Simona, Salmaggi, Andrea, Sangalli, Davide, Zini, Andrea, Doijiri, Ryosuke, Fukuda, Hiroki, Fujinaka, Toshiyuki, Fujita, Kyohei, Imamura, Hirotoshi, Sakai, Nobuyuki, Kanamaru, Takuya, Kimura, Naoto, Kono, Ryuhei, Miyake, Kosuke, Sakaguchi, Manabu, Sakai, Kenichiro, Sonoda, Kazutaka, Todo, Kenichi, Miyashita, Fumio, Tokuda, Naoki, Matsumaru, Yuji, Matsumoto, Shoji, Ohara, Nobuyuki, Shindo, Seigo, Takenobu, Yohei, Yoshimoto, Takeshi, Toyoda, Kazunori, Uwatoko, Takeshi, Yagita, Yoshiki, Yamada, Takehiro, Yamamoto, Nobuaki, Yamamoto, Ryoo, Yazawa, Yukako, Sugiura, Yuri, Waweru, Peter Kuria, Baek, Jang-Hyun, Lee, Si Baek, Seo, Kwon-Duk, Sohn, Sung-Il, Arsovska, Anita Ante, Chan, Yong Chieh, Wan Zaidi, Wan Asyraf, Jaafar, Ainul Syahrilfazli, Gongora-Rivera, Fernando, Martinez-Marino, Manuel, Infante-Valenzuela, Adrian, Groppa, Stanislav, Leahu, Pavel, Coutinho, Jonathan M, Rinkel, Leon A., Dippel, Diederik W. J., Dam-Nolen, Dianne H. K. van, Ranta, Annemarei, Wu, Teddy Y., Adebayo, Tajudeen Temitayo, Bello, Abiodun H., Nwazor, Ernest Okwundu, Sunmonu, Taofiki Ajao, Wahab, Kolawole Wasiu, Ronning, Ole Morten, Sandset, Else Charlotte, Al Hashmi, Amal M., Ahmad, Saima, Rashid, Umair, Rodriguez-Kadota, Liliana, Vences, Miguel Ángel, Yalung, Patrick Matic, Hao Dy, Jon Stewart, Pineda-Franks, Maria Carissa, Co, Christian Oliver, Brola, Waldemar, Debiec, Aleksander, Dorobek, Malgorzata, Karlinski, Michal Adam, Labuz-Roszak, Beata M., Lasek-Bal, Anetta, Sienkiewicz-Jarosz, Halina, Staszewski, Jacek, Sobolewski, Piotr, Wiacek, Marcin, Zielinska-Turek, Justyna, Araujo, Andre Pinho, Rocha, Mariana, Castro, Pedro, Cruz, Vitor Tedim, Ferreira, Paulo Venancio, Ferreira, Patricia, Nunes, Ana Paiva, Fonseca, Luisa, Marto, João Pedro, Pinho E Melo, Teresa, Rodrigues, Miguel, Silva, M. Luis, Dimitriade, Adela, Falup-Pecurariu, Cristian, Hamid, May Adel, Venketasubramanian, Narayanaswamy, Krastev, Georgi, Mako, Miroslav, Ayo-Martin, Oscar, Hernández-Fernández, Francisco, Blasco, Jordi, Rodríguez-Vázquez, Alejandro, Cruz-Culebras, Antonio, Moniche, Francisco, Montaner, Joan, Pérez-Sánchez, Soledad, García Sánchez, María Jesús, Guillán Rodríguez, Marta, Jood, Katarina, Nordanstig, Annika, Mazya, Michael V., Moreira, Tiago T. P., Bernava, Gianmarco, Beyeler, Morin, Bolognese, Manuel, Carrera, Emmanuel, Dobrocky, Tomas, Karwacki, Grzegorz Marek, Keller, Emanuela, Hsieh, Chang Yang, Boonyakarnkul, Surawan, Churojana, Anchalee, Aykac, Ozlem, Ozdemir, Atilla Ã-Zcan, Bajrami, Arsida, Senadim, Songul, Hussain, Syed Irteza, John, Seby, Banerjee, Soma, Kwan, Joseph, Krishnan, Kailash, Lenthall, Robert, Matthews, Ashok, Wong, Ken, Zhang, Liqun, Altschul, Dorothea, Asif, Kaiz S., Bahiru, Zeelalem, Below, Kristine, Biller, José, Ruland, Sean, Chaudry, Saqib A., Chen, Michael, Chebl, Alex, Cibulka, Jackie, Cistrunk, Leon, Clark, Judith, Colasurdo, Marco, Czap, Alexandra, de Havenon, Adam, D'Amato, Salvatore, Dharmadhikari, Sushrut, Grimmett, Kasey B., Dmytriw, Adam A., Etherton, Mark R., Ezepue, Chizoba, Farooqui, Mudassir, Feske, Steven K., Fink, Lauren, Gasimova, Ulviyya, Guzik, Amy K., Hakemi, Maryam, Hovingh, Majesta, Khan, Muhib, Jillela, Dinesh, Kan, Peter T., Khatri, Rakesh, Khawaja, Ayaz M., Khoury, Naim N., Kiley, Nicole L., Kim, Benny S., Kolikonda, Murali K., Kuhn, Anna Luisa, Lara, Stephanie, Linares, Guillermo, Linfante, Italo, Lukovits, Timothy G., Lycan, Sarah, Male, Shailesh S., Maali, Laith, Mancin, John, Masoud, Hesham, Mohamed, Ghada A., Monteiro, Andre, Nahab, Fadi, Nalleballe, Krishna, Ortega-Gutiérrez, Santiago, Puri, Ajit S, Radaideh, Yazan, Rahangdale, Rahul H., Rai, Ansaar, Ramakrishnan, Pankajavalli, Reddy, Aravind B., Rojas-Soto, Diana M., Romero, José Rafael, Rost, Natalia S., Rothstein, Aaron, Omran, Setareh Salehi, Sheth, Sunil A., Siddiqui, Adnan H., Starosciak, Amy K., Tarlov, Nicholas E., Taylor, Robert A., Wang, Michael J., Wolfe, Jared, Wong, Ka-Ho, Le, Huynh Vu, Nguyen, Quy Viet, Pham, Thong Nhu, Nguyen, Trung Thanh, Phan, Hoang Thi, Ton, Mai Duy, Fischer, Urs, Michel, Patrik, Strambo, Davide, Martins, Sheila O., Zaidat, Osama O., and Nogueira, Raul G.

Abstract

Background and Objectives: Declines in stroke admission, IV thrombolysis (IVT), and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the effect of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), IVT, and mechanical thrombectomy over a 1-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020)., Methods: We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, IVT treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases., Results: There were 148,895 stroke admissions in the 1 year immediately before compared with 138,453 admissions during the 1-year pandemic, representing a 7% decline (95% CI [95% CI 7.1–6.9]; p < 0.0001). ICH volumes declined from 29,585 to 28,156 (4.8% [5.1–4.6]; p < 0.0001) and IVT volume from 24,584 to 23,077 (6.1% [6.4–5.8]; p < 0.0001). Larger declines were observed at high-volume compared with low-volume centers (all p < 0.0001). There was no significant change in mechanical thrombectomy volumes (0.7% [0.6–0.9]; p = 0.49). Stroke was diagnosed in 1.3% [1.31–1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82–2.97], 5,656/195,539) of all stroke hospitalizations., Discussion: There was a global decline and shift to lower-volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared with the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year., Trial Registration Information: This study is registered under NCT04934020.

Published
2023

10. ENHANCE CYBER ATTACK DETECTION ACCURACY BY HYBRID ATTENTION NEURAL NETWORK

Author

Manideep Beesetty, Rambabu Pemula, RVV Murali Krishna, P Praveen Kumar, Madireddy, Bhavani, and Susmitha Jillela

Subjects
Cyber-Attacks, Cyber-Security, Data Encoding, Filtering, Normalization, Deep Learning Models
Abstract

An attempt by cyber fraud to use one or more machines across one or many networks is known as a cyber-attack. Not every website that a person visits can be completely safe from cyberattacks. Thus, the user must assure cyber-security. By creating two distinct Deep Learning (DL) models that can recognize the existence of a cyberattack on a website, this study seeks to aid the user. For this purpose, the NSL-KDD dataset is used to build a database with more than 40 features related to the detection of a cyberattack. For optimal performance, the database has been preprocessed. Normalization, data encoding, and statistical highlighting are all parts of the preprocessing. The preprocessed dataset is then divided into its training and testing halves. Two different deep-learning algorithms generated two distinct models. Convolutional Neural Network (CNN) and Hybrid Attention Neural Network (HANN) are the algorithms employed in this investigation. The most effective DL model for detecting a cyberattack is identified once it has been trained and tested. Both models displayed various performance categories while being trained. The accuracy values of the model created using the CNN algorithm increased virtually linearly, reaching a point with an accuracy of more than 90% after the 14th epoch. Yet, during the 6th epoch alone, the model created using the HANN algorithm achieves an accuracy higher than 90%. During the first training period, the HANN model's highest loss value is just under 90%. The final accuracy of the model created using the CNN algorithm is 90.8%, whereas the accuracy of the HANN model is 95.8%, according to testing. In the end, it may be said that the HANN algorithm is more effective at spotting cyber-attack than CNN. This concept may one day be implemented as a backend processor for a website that verifies the legitimacy of other websites and software.

Published
2023
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12. Global Impact of the COVID-19 Pandemic on Cerebral Venous Thrombosis and Mortality

Author

Nguyen, Thanh N, Qureshi, Muhammad M, Klein, Piers, Yamagami, Hiroshi, Abdalkader, Mohamad, Mikulik, Robert, Sathya, Anvitha, Mansour, Ossama Yassin, Czlonkowska, Anna, Hannah, Lo, Field, Thalia S, Charidimou, Andreas, Banerjee, Soma, Yaghi, Shadi, Siegler, James E, Sedova, Petra, Kwan, Joseph, de Sousa, Diana Aguiar, Demeestere, Jelle, Inoa, Violiza, Omran, Setareh Salehi, Zhang, Liqun, Michel, Patrik, Strambo, Davide, Marto, João Pedro, Nogueira, Raul G, Kristoffersen, Espen Saxhaug, Tsivgoulis, Georgios, Lereis, Virginia Pujol, Alice, Ma, Enzinger, Christian, Gattringer, Thomas, Rahman, Aminur, Bonnet, Thomas, Ligot, Noémie, De Raedt, Sylvie, Lemmens, Robin, Vanacker, Peter, Vandervorst, Fenne, Conforto, Adriana Bastos, Hidalgo, Raquel C T, Mora Cuervo, Daissy Liliana, de Oliveira Neves, Luciana, Lameirinhas da Silva, Isabelle, Martíns, Rodrigo Targa, Rebello, Letícia C, Santiago, Igor Bessa, Sadelarova, Teodora, Kalpachki, Rosen, Alexiev, Filip, Cora, Elena Adela, Kelly, Michael E, Peeling, Lissa, Pikula, Aleksandra, Chen, Hui-Sheng, Chen, Yimin, Yang, Shuiquan, Roje Bedekovic, Marina, Čabal, Martin, Tenora, Dusan, Fibrich, Petr, Dušek, Pavel, Hlaváčová, Helena, Hrabanovska, Emanuela, Jurák, Lubomír, Kadlčíková, Jana, Karpowicz, Igor, Klečka, Lukáš, Kovář, Martin, Neumann, Jiří, Paloušková, Hana, Reiser, Martin, Rohan, Vladimir, Šimůnek, Libor, Skoda, Ondreij, Škorňa, Miroslav, Šrámek, Martin, Drenck, Nicolas, Sobh, Khalid, Lesaine, Emilie, Sabben, Candice, Reiner, Peggy, Rouanet, Francois, Strbian, Daniel, Boskamp, Stefan, Mbroh, Joshua, Nagel, Simon, Rosenkranz, Michael, Poli, Sven, Thomalla, Götz, Karapanayiotides, Theodoros, Koutroulou, Ioanna, Kargiotis, Odysseas, Palaiodimou, Lina, Barrientos Guerra, José Dominguo, Huded, Vikram, Nagendra, Shashank, Prajapati, Chintan, Sylaja, P N, Sani, Achmad Firdaus, Ghoreishi, Abdoreza, Farhoudi, Mehdi, Sadeghi Hokmabadi, Elyar, Hashemilar, Mazyar, Sabetay, Sergiu Ionut, Rahal, Fadi, Acampa, Maurizio, Adami, Alessandro, Longoni, Marco, Ornello, Raffaele, Renieri, Leonardo, Romoli, Michele, Sacco, Simona, Salmaggi, Andrea, Sangalli, Davide, Zini, Andrea, Sakai, Kenichiro, Fukuda, Hiroki, Fujita, Kyohei, Imamura, Hirotoshi, Kosuke, Miyake, Sakaguchi, Manabu, Sonoda, Kazutaka, Matsumaru, Yuji, Ohara, Nobuyuki, Shindo, Seigo, Takenobu, Yohei, Yoshimoto, Takeshi, Toyoda, Kazunori, Uwatoko, Takeshi, Sakai, Nobuyuki, Yamamoto, Nobuaki, Yamamoto, Ryoo, Yazawa, Yukako, Sugiura, Yuri, Baek, Jang-Hyun, Lee, Si Baek, Seo, Kwon-Duk, Sohn, Sung-Il, Lee, Jin Soo, Arsovska, Anita Ante, Chieh, Chan Yong, Wan Zaidi, Wan Asyraf, Wan Yahya, Wan Nur Nafisah, Gongora-Rivera, Fernando, Martinez-Marino, Manuel, Infante-Valenzuela, Adrian, Dippel, Diederik, van Dam-Nolen, Dianne H K, Teddy Y, Wu, Punter, Martin, Adebayo, Tajudeen Temitayo, Bello, Abiodun H, Sunmonu, Taofiki Ajao, Wahab, Kolawole Wasiu, Sundseth, Antje, Al Hashmi, Amal M, Ahmad, Saima, Rashid, Umair, Rodriguez-Kadota, Liliana, Vences, Miguel Ángel, Yalung, Patrick Matic, Jon Stewart Hao, Dy, Brola, Waldemar, Dębiec, Aleksander, Dorobek, Malgorzata, Karlinski, Michal Adam, Labuz-Roszak, Beata M, Lasek-Bal, Anetta, Sienkiewicz-Jarosz, Halina, Staszewski, Jacek, Sobolewski, Piotr, Wiącek, Marcin, Zielinska-Turek, Justyna, Araújo, André Pinho, Rocha, Mariana, Castro, Pedro, Ferreira, Patricia, Nunes, Ana Paiva, Fonseca, Luísa, Pinho E Melo, Teresa, Rodrigues, Miguel, Silva, M Luis, Ciopleias, Bogdan, Dimitriade, Adela, Falup-Pecurariu, Cristian, Hamid, May Adel, Venketasubramanian, Narayanaswamy, Krastev, Georgi, Haring, Jozef, Ayo-Martin, Oscar, Hernandez-Fernandez, Francisco, Blasco, Jordi, Rodríguez-Vázquez, Alejandro, Cruz-Culebras, Antonio, Moniche, Francisco, Montaner, Joan, Perez-Sanchez, Soledad, García Sánchez, María Jesús, Guillán Rodríguez, Marta, Bernava, Gianmarco, Bolognese, Manuel, Carrera, Emmanuel, Churojana, Anchalee, Aykac, Ozlem, Özdemir, Atilla Özcan, Bajrami, Arsida, Senadim, Songul, Hussain, Syed I, John, Seby, Krishnan, Kailash, Lenthall, Robert, Asif, Kaiz S, Below, Kristine, Biller, Jose, Chen, Michael, Chebl, Alex, Colasurdo, Marco, Czap, Alexandra, de Havenon, Adam H, Dharmadhikari, Sushrut, Eskey, Clifford J, Farooqui, Mudassir, Feske, Steven K, Goyal, Nitin, Grimmett, Kasey B, Guzik, Amy K, Haussen, Diogo C, Hovingh, Majesta, Jillela, Dinesh, Kan, Peter T, Khatri, Rakesh, Khoury, Naim N, Kiley, Nicole L, Kolikonda, Murali K, Lara, Stephanie, Grace, Li, Linfante, Italo, Loochtan, Aaron I, Lopez, Carlos D, Lycan, Sarah, Male, Shailesh S, Nahab, Fadi, Maali, Laith, Masoud, Hesham E, Min, Jiangyong, Orgeta-Gutierrez, Santiago, Mohamed, Ghada A, Mohammaden, Mahmoud, Nalleballe, Krishna, Radaideh, Yazan, Ramakrishnan, Pankajavalli, Rayo-Taranto, Bliss, Rojas-Soto, Diana M, Ruland, Sean, Simpkins, Alexis N, Sheth, Sunil A, Starosciak, Amy K, Tarlov, Nicholas E, Taylor, Robert A, Voetsch, Barbara, Zhang, Linda, Duong, Hai Quang, Dao, Viet-Phuong, Huynh Vu, Le, Pham, Thong Nhu, Ton, Mai Duy, Tran, Anh Duc, Zaidat, Osama O, Machi, Paolo, Dirren, Elisabeth, Rodríguez Fernández, Claudio, Escartín López, Jorge, Fernández Ferro, Jose Carlos, Mohammadzadeh, Niloofar, Suryadevara, Neil C, de la Cruz Fernández, Beatriz, Bessa, Filipe, Jancar, Nina, Brady, Megan, Scozzari, Dawn, SVIN COVID-19 Global COVID Stroke Registry, Neurology, Radiology & Nuclear Medicine, Society of Vascular and Interventional Neurology, Nguyen, Thanh N., Nogueira, Raul G., Clinical sciences, Neuroprotection & Neuromodulation, Biology, Neurologian yksikkö, HUS Neurocenter, and Helsinki University Hospital Area

Subjects
Stroke, Vaccine-induced immune thrombotic thrombocytopenia, SDG 3 - Good Health and Well-being, Cerebral venous thrombosis, SARS-CoV-2, 3112 Neurosciences, COVID-19, Mortality, Neurology (clinical), Cardiology and Cardiovascular Medicine, 3124 Neurology and psychiatry
Abstract

[Background and Purpose] Recent studies suggested an increased incidence of cerebral venous thrombosis (CVT) during the coronavirus disease 2019 (COVID-19) pandemic. We evaluated the volume of CVT hospitalization and in-hospital mortality during the 1st year of the COVID-19 pandemic compared to the preceding year., [Methods] We conducted a cross-sectional retrospective study of 171 stroke centers from 49 countries. We recorded COVID-19 admission volumes, CVT hospitalization, and CVT in-hospital mortality from January 1, 2019, to May 31, 2021. CVT diagnoses were identified by International Classification of Disease-10 (ICD-10) codes or stroke databases. We additionally sought to compare the same metrics in the first 5 months of 2021 compared to the corresponding months in 2019 and 2020 (ClinicalTrials.gov Identifier: NCT04934020)., [Results] There were 2,313 CVT admissions across the 1-year pre-pandemic (2019) and pandemic year (2020); no differences in CVT volume or CVT mortality were observed. During the first 5 months of 2021, there was an increase in CVT volumes compared to 2019 (27.5%; 95% confidence interval [CI], 24.2 to 32.0; P, [Conclusions] During the 1st year of the COVID-19 pandemic, CVT hospitalization volume and CVT in-hospital mortality did not change compared to the prior year. COVID-19 diagnosis was associated with higher CVT in-hospital mortality. During the first 5 months of 2021, there was an increase in CVT hospitalization volume and increase in CVT-related mortality, partially attributable to VITT., The study was funded by the Society of Vascular and Interventional Neurology research pilot grant.

Published
2022

13. Global impact of the COVID-19 pandemic on stroke volumes and cerebrovascular events : a 1-year follow-up

Author

Nguyen, Thanh N., Qureshi, Muhammad M., Klein, Piers, Yamagami, Hiroshi, Mikulik, Robert, Czlonkowska, Anna, Abdalkader, Mohamad, Sedova, Petra, Sathya, Anvitha, Lo, Hannah C., Mansour, Ossama Yassin, Vanguru, Husitha Reddy, Lesaine, Emilie, Tsivgoulis, Georgios, Loochtan, Aaron, I, Demeestere, Jelle, Uchino, Ken, Inoa, Violiza, Goyal, Nitin, Charidimou, Andreas, Siegler, James E., Yaghi, Shadi, de Sousa, Diana Aguiar, Mohammaden, Mahmoud H., Haussen, Diogo C., Kristoffersen, Espen Saxhaug, Lereis, Virginia Pujol, Scollo, Sergio Daniel, Campbell, Bruce C.V., Ma, Alice, Thomas, James Orton, Parsons, Mark W., Singhal, Shaloo, Slater, Lee-Anne, Martins, Rodrigo Tomazini, Enzinger, Chris, Gattringer, Thomas, Rahman, Aminur, Bonnet, Thomas, Ligot, Noemie, De Raedt, Sylvie, Lemmens, Robin, Vanacker, Peter, Vandervorst, Fenne, Conforto, Adriana Bastos, Hidalgo, Raquel C.T., de Neves, Luciana Oliveira, Martins, Rodrigo Targa, Mora Cuervo, Daissy Liliana, Rebello, Leticia C., Santiago, Igor Bessa, de Silva, Isabelle Lameirinhas, Sakelarova, Teodora, Kalpachki, Rosen, Alexiev, Filip, Catanese, Luciana, Cora, Elena Adela, Goyal, Mayank, Hill, Michael D., Kelly, Michael E., Khosravani, Houman, Lavoie, Pascale, Peeling, Lissa, Pikula, Aleksandra, Rivera, Rodrigo, Chen, Hui-Sheng, Chen, Yimin, Huo, Xiaochuan, Miao, Zhongrong, Yang, Shuiquan, Bedekovic, Marina Roje, Bralic, Marina, Budincevic, Hrvoje, Corredor-Quintero, Angel Basilio, Lara-Sarabia, Osvaldo E., Cabal, Martin, Tenora, Dusan, Fibrich, Petr, Herzig, Roman, Hrabanovska, Emanuela, Hlinovsky, David, Jurak, Lubomir, Kadlcikova, Jana, Karpowicz, Igor, Klecka, Lukas, Kovar, Martin, Lauer, David, Neumann, Jiri, Palouskova, Hana, Reiser, Martin, Rekova, Petra, Rohan, Vladimir, Skoda, Ondrej, Skorna, Miroslav, Sramek, Martin, Zakova, Lenka, Christensen, Hanne, Drenck, Nicolas, Iversen, Helle Klingenberg, Truelsen, Thomas Clement, Wienecke, Troels, Sobh, Khalid, Ylikotila, Pauli, Alpay, Kemal, Strbian, Daniel, Bernady, Patricia, Casenave, Philippe, Dan, Maria, Faucheux, Jean-Marc, Gentric, Jean-Christophe, Magro, Elsa, Sabben, Candice, Reiner, Peggy, Rouanet, Francois, Bohmann, Ferdinand O., Boskamp, Stefan, Mbroh, Joshua, Nagel, Simon, Nolte, Christian H., Ringleb, Peter A., Rosenkranz, Michael, Poli, Sven, Thomalla, Gotz, Karapanayiotides, Theodoros, Koutroulou, Ioanna, Kargiotis, Odysseas, Palaiodimou, Lina, Barrientos Guerra, Jose Dominguo, Huded, Vikram, Menon, Bindu, Nagendra, Shashank, Prajapati, Chintan, Sylaja, P.N., Pramana, Nyoman Angga Krishna, Sani, Achmad Firdaus, Ghoreishi, Abdoreza, Farhoudi, Mehdi, Hokmabadi, Elyar Sadeghi, Abu Raya, Tariq, Kalmanovich, Shani Avnery, Ronen, Levite, Sabetay, Sergiu Ionut, Acampa, Maurizio, Adami, Alessandro, Castellan, Lucio, Longoni, Marco, Ornello, Raffaele, Renieri, Leonardo, Bigliani, Claudia Rolla, Romoli, Michele, Sacco, Simona, Salmaggi, Andrea, Sangalli, Davide, Zini, Andrea, Doijiri, Ryosuke, Fukuda, Hiroki, Fujinaka, Toshiyuki, Fujita, Kyohei, Imamura, Hirotoshi, Sakai, Nobuyuki, Kanamaru, Takuya, Kimura, Naoto, Kono, Ryuhei, Miyake, Kosuke, Sakaguchi, Manabu, Sakai, Kenichiro, Sonoda, Kazutaka, Todo, Kenichi, Miyashita, Fumio, Tokuda, Naoki, Matsumaru, Yuji, Matsumoto, Shoji, Ohara, Nobuyuki, Shindo, Seigo, Takenobu, Yohei, Yoshimoto, Takeshi, Toyoda, Kazunori, Uwatoko, Takeshi, Yagita, Yoshiki, Yamada, Takehiro, Yamamoto, Nobuaki, Yamamoto, Ryoo, Yazawa, Yukako, Sugiura, Yuri, Waweru, Peter Kuria, Baek, Jang-Hyun, Lee, Si Baek, Seo, Kwon-Duk, Sohn, Sung-Il, Arsovska, Anita Ante, Chan, Yong Chieh, Zaidi, Wan Asyraf Wan, Jaafar, Ainul Syahrilfazli, Gongora-Rivera, Fernando, Martinez-Marino, Manuel, Infante-Valenzuela, Adrian, Groppa, Stanislav, Leahu, Pavel, Coutinho, Jonathan M., Rinkel, Leon A., Dippel, Diederik W.J., Van dam-Nolen, Dianne H.K., Ranta, Annemarei, Wu, Teddy Y., Adebayo, Tajudeen Temitayo, Bello, Abiodun H., Nwazor, Ernest Okwundu, Sunmonu, Taofiki Ajao, Wahab, Kolawole Wasiu, Ronning, Ole Morten, Sandset, Else Charlotte, Al Hashmi, Amal M., Ahmad, Saima, Rashid, Umair, Rodriguez-Kadota, Liliana, Yalung, Patrick Matic, Dy, Jon Stewart Hao, Pineda-Franks, Maria Carissa, Co, Christian Oliver, Brola, Waldemar, Debiec, Aleksander, Dorobek, Malgorzata, Karlinski, Michal Adam, Labuz-Roszak, Beata M., Lasek-Bal, Anetta, Sienkiewicz-Jarosz, Halina, Staszewski, Jacek, Sobolewski, Piotr, Wiacek, Marcin, Zielinska-Turek, Justyna, Araujo, Andre Pinho, Rocha, Mariana, Castro, Pedro, Cruz, Vitor Tedim, Ferreira, Paulo Venancio, Ferreira, Patricia, Nunes, Ana Paiva, Fonseca, Luisa, Marto, Joao Pedro, Pinho e Melo, Teresa, Rodrigues, Miguel, Silva, M. Luis, Dimitriade, Adela, Falup-Pecurariu, Cristian, Hamid, May Adel, Venketasubramanian, Narayanaswamy, Krastev, Georgi, Mako, Miroslav, Ayo-Martin, Oscar, Hernandez-Ferandez, Francisco, Blasco, Jordi, Rodriguez-Vazquez, Alejandro, Cruz-Culebras, Antonio, Moniche, Francisco, Montaner, Joan, Perez-Sanchez, Soledad, Garcia Sanchez, Maria Jesas, Guillan Rodriguez, Marta, Jood, Katarina, Nordanstig, Annika, Mazya, Michael V., Moreira, Tiago T.P., Bernava, Gianmarco, Beyeler, Morin, Bolognese, Manuel, Carrera, Emmanuel, Dobrocky, Tomas, Karwacki, Grzegorz Marek, Keller, Emanuela, Hsieh, Chang Yang, Boonyakarnkul, Surawan, Churojana, Anchalee, Aykac, Ozlem, Ozdemir, Atilla Azcan, Bajrami, Arsida, Senadim, Songul, Hussain, Syed Irteza, John, Seby, Banerjee, Soma, Kwan, Joseph, Krishnan, Kailash, Lenthall, Robert, Matthews, Ashok, Wong, Ken, Zhang, Liqun, Altschul, Dorothea, Asif, Kaiz S., Bahiru, Zeelalem, Below, Kristine, Ruland, Sean, Chaudry, Saqib A., Chen, Michael, Chebl, Alex, Cibulka, Jackie, Cistrunk, Leon, Clark, Judith, Colasurdo, Marco, Czap, Alexandra, de Havenon, Adam, D'Amato, Salvatore, Dharmadhikari, Sushrut, Grimmett, Kasey B., Dmytriw, Adam A., Etherton, Mark R., Ezepue, Chizoba, Farooqui, Mudassir, Feske, Steven K., Fink, Lauren, Gasimova, Ulviyya, Guzik, Amy K., Hakemi, Maryam, Hovingh, Majesta, Khan, Muhib, Jillela, Dinesh, Khatri, Rakesh, Khawaja, Ayaz M., Khoury, Naim N., Kiley, Nicole L., Kim, Benny S., Kolikonda, Murali K., Kuhn, Anna Luisa, Lara, Stephanie, Linares, Guillermo, Linfante, Italo, Lukovits, Timothy G., Lycan, Sarah, Male, Shailesh S., Maali, Laith, Mancin, John, Masoud, Hesham, Mohamed, Ghada A., Monteiro, Andre, Nahab, Fadi, Nalleballe, Krishna, Ortega-Gutierrez, Santiago, Puri, Ajit S., Radaideh, Yazan, Rahangdale, Rahul H., Rai, Ansaar, Ramakrishnan, Pankajavalli, Reddy, Aravind B., Rojas-Soto, Diana M., Romero, Jose Rafael, Rost, Natalia S., Rothstein, Aaron, Omran, Setareh Salehi, Sheth, Sunil A., Siddiqui, Adnan H., Starosciak, Amy K., Tarlov, Nicholas E., Taylor, Robert A., Wang, Michael J., Wolfe, Jared, Wong, Ka-Ho, Huynh Vu Le, Quy Viet Nguyen, Thong Nhu Pham, Trung Thanh Nguyen, Hoang Thi Phan, Mai Duy Ton, Fischer, Urs, Michel, Patrik, Strambo, Davide, Martins, Sheila O., Zaidat, Osama O., Nogueira, Raul G., SVIN COVID-19 Global Stroke Registry, Neurology, ACS - Atherosclerosis & ischemic syndromes, ANS - Neurovascular Disorders, Graduate School, and Radiology & Nuclear Medicine

Subjects
Neurology (clinical), Human medicine
Abstract

Background and ObjectivesDeclines in stroke admission, IV thrombolysis (IVT), and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the effect of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), IVT, and mechanical thrombectomy over a 1-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020).MethodsWe conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, IVT treatments, and mechanical thrombectomy procedures. Diagnoses were identified by theirICD-10codes or classifications in stroke databases.ResultsThere were 148,895 stroke admissions in the 1 year immediately before compared with 138,453 admissions during the 1-year pandemic, representing a 7% decline (95% CI [95% CI 7.1–6.9];p< 0.0001). ICH volumes declined from 29,585 to 28,156 (4.8% [5.1–4.6];p< 0.0001) and IVT volume from 24,584 to 23,077 (6.1% [6.4–5.8];p< 0.0001). Larger declines were observed at high-volume compared with low-volume centers (allp< 0.0001). There was no significant change in mechanical thrombectomy volumes (0.7% [0.6–0.9];p= 0.49). Stroke was diagnosed in 1.3% [1.31–1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82–2.97], 5,656/195,539) of all stroke hospitalizations.DiscussionThere was a global decline and shift to lower-volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared with the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year.Trial Registration InformationThis study is registered underNCT04934020.

Published
2023

14. Global impact of the COVID-19 pandemic on stroke volumes and cerebrovascular events

Author

Nguyen, Thanh N., Qureshi, Muhammad M., Klein, Piers, Yamagami, Hiroshi, Mikulik, Robert, Czlonkowska, Anna, Abdalkader, Mohamad, Sedova, Petra, Sathya, Anvitha, Lo, Hannah C., Mansour, Ossama Yassin, Iversen, Helle Klingenberg, Truelsen, Thomas Clement, Wienecke, Troels, Sobh, Khalid, Ylikotila, Pauli, Alpay, Kemal, Strbian, Daniel, Bernady, Patricia, Casenave, Philippe, Dan, Maria, Vanguru, Husitha Reddy, Faucheux, Jean-Marc, Gentric, Jean-Christophe, Magro, Elsa, Sabben, Candice, Reiner, Peggy, Rouanet, Francois, Bohmann, Ferdinand O., Boskamp, Stefan, Mbroh, Joshua, Nagel, Simon, Lesaine, Emilie, Nolte, Christian H., Ringleb, Peter A., Rosenkranz, Michael, Poli, Sven, Thomalla, Götz, Karapanayiotides, Theodoros, Koutroulou, Ioanna, Kargiotis, Odysseas, Palaiodimou, Lina, Barrientos Guerra, Jose Dominguo, Tsivgoulis, Georgios, Huded, Vikram, Menon, Bindu, Nagendra, Shashank, Prajapati, Chintan, Sylaja, P.N., Krishna Pramana, Nyoman Angga, Sani, Achmad Firdaus, Ghoreishi, Abdoreza, Farhoudi, Mehdi, Hokmabadi, Elyar Sadeghi, Loochtan, Aaron I., Raya, Tariq Abu, Kalmanovich, Shani Avnery, Ronen, Levite, Sabetay, Sergiu Ionut, Acampa, Maurizio, Adami, Alessandro, Castellan, Lucio, Longoni, Marco, Ornello, Raffaele, Renieri, Leonardo, Demeestere, Jelle, Bigliani, Claudia Rolla, Romoli, Michele, Sacco, Simona, Salmaggi, Andrea, Sangalli, Davide, Zini, Andrea, Doijiri, Ryosuke, Fukuda, Hiroki, Fujinaka, Toshiyuki, Fujita, Kyohei, Uchino, Ken, Imamura, Hirotoshi, Sakai, Nobuyuki, Kanamaru, Takuya, Kimura, Naoto, Kono, Ryuhei, Miyake, Kosuke, Sakaguchi, Manabu, Sakai, Kenichiro, Sonoda, Kazutaka, Todo, Kenichi, Inoa, Violiza, Miyashita, Fumio, Tokuda, Naoki, Matsumaru, Yuji, Matsumoto, Shoji, Ohara, Nobuyuki, Shindo, Seigo, Takenobu, Yohei, Yoshimoto, Takeshi, Toyoda, Kazunori, Uwatoko, Takeshi, Goyal, Nitin, Yagita, Yoshiki, Yamada, Takehiro, Yamamoto, Nobuaki, Yamamoto, Ryoo, Yazawa, Yukako, Sugiura, Yuri, Waweru, Peter Kuria, Baek, Jang-Hyun, Lee, Si Baek, Seo, Kwon-Duk, Charidimou, Andreas, Sohn, Sung-Il, Arsovska, Anita Ante, Chan, Yong Chieh, Wan Zaidi, Wan Asyraf, Jaafar, Ainul Syahrilfazli, Gongora-Rivera, Fernando, Martinez-Marino, Manuel, Infante-Valenzuela, Adrian, Groppa, Stanislav, Leahu, Pavel, Siegler, James E., Coutinho, Jonathan M., Rinkel, Leon A., Dippel, Diederik W.J., van Dam-Nolen, Dianne H.K., Ranta, Annemarei, Wu, Teddy Y., Adebayo, Tajudeen Temitayo, Bello, Abiodun H., Nwazor, Ernest Okwundu, Sunmonu, Taofiki Ajao, Yaghi, Shadi, Wahab, Kolawole Wasiu, Ronning, Ole Morten, Sandset, Else Charlotte, Al Hashmi, Amal M., Ahmad, Saima, Rashid, Umair, Rodriguez-Kadota, Liliana, Vences, Miguel Ángel, Yalung, Patrick Matic, Hao Dy, Jon Stewart, de Sousa, Diana Aguiar, Pineda-Franks, Maria Carissa, Co, Christian Oliver, Brola, Waldemar, Debiec, Aleksander, Dorobek, Malgorzata, Karlinski, Michal Adam, Labuz-Roszak, Beata M., Lasek-Bal, Anetta, Sienkiewicz-Jarosz, Halina, Staszewski, Jacek, Mohammaden, Mahmoud H., Sobolewski, Piotr, Wiacek, Marcin, Zielinska-Turek, Justyna, Araujo, Andre Pinho, Rocha, Mariana, Castro, Pedro, Cruz, Vitor Tedim, Ferreira, Paulo Venancio, Ferreira, Patricia, Nunes, Ana Paiva, Haussen, Diogo C., Fonseca, Luisa, Marto, João Pedro, Melo, Teresa Pinho e, Rodrigues, Miguel, Silva, M. Luis, Dimitriade, Adela, Falup-Pecurariu, Cristian, Hamid, May Adel, Venketasubramanian, Narayanaswamy, Krastev, Georgi, Kristoffersen, Espen Saxhaug, Mako, Miroslav, Ayo-Martin, Oscar, Hernández-Fernández, Francisco, Blasco, Jordi, Rodríguez-Vázquez, Alejandro, Cruz-Culebras, Antonio, Moniche, Francisco, Montaner, Joan, Perez-Sanchez, Soledad, García Sánchez, María Jesús, Lereis, Virginia Pujol, Guillán Rodríguez, Marta, Jood, Katarina, Nordanstig, Annika, Mazya, Michael V., Moreira, Tiago T.P., Bernava, Gianmarco, Beyeler, Morin, Bolognese, Manuel, Carrera, Emmanuel, Dobrocky, Tomas, Scollo, Sergio Daniel, Karwacki, Grzegorz Marek, Keller, Emanuela, Hsieh, Chang Yang, Boonyakarnkul, Surawan, Churojana, Anchalee, Aykac, Ozlem, Ozdemir, Atilla Özcan, Bajrami, Arsida, Senadim, Songul, Hussain, Syed Irteza, Campbell, Bruce C. V., John, Seby, Banerjee, Soma, Kwan, Joseph, Krishnan, Kailash, Lenthall, Robert, Matthews, Ashok, Wong, Ken, Zhang, Liqun, Altschul, Dorothea, Asif, Kaiz S., Ma, Alice, Bahiru, Zeelalem, Below, Kristine, Biller, José, Ruland, Sean, Chaudry, Saqib A., Chen, Michael, Chebl, Alex, Cibulka, Jackie, Cistrunk, Leon, Clark, Judith, Thomas, James Orton, Colasurdo, Marco, Czap, Alexandra, de Havenon, Adam, D'Amato, Salvatore, Dharmadhikari, Sushrut, Grimmett, Kasey B., Dmytriw, Adam A., Etherton, Mark R., Ezepue, Chizoba, Farooqui, Mudassir, Parsons, Mark W., Feske, Steven K., Fink, Lauren, Gasimova, Ulviyya, Guzik, Amy K., Hakemi, Maryam, Hovingh, Majesta, Khan, Muhib, Jillela, Dinesh, Kan, Peter T., Khatri, Rakesh, Singhal, Shaloo, Khawaja, Ayaz M., Khoury, Naim N., Kiley, Nicole L., Kim, Benny S., Kolikonda, Murali K., Kuhn, Anna Luisa, Lara, Stephanie, Linares, Guillermo, Linfante, Italo, Lukovits, Timothy G., Slater, Lee-Anne, Lycan, Sarah, Male, Shailesh S., Maali, Laith, Mancin, John, Masoud, Hesham, Mohamed, Ghada A., Monteiro, Andre, Nahab, Fadi, Nalleballe, Krishna, Ortega-Gutierrez, Santiago, Tomazini Martins, Rodrigo, Puri, Ajit S., Radaideh, Yazan, Rahangdale, Rahul H., Rai, Ansaar, Ramakrishnan, Pankajavalli, Reddy, Aravind B., Rojas-Soto, Diana M., Romero, Jose Rafael, Rost, Natalia S., Rothstein, Aaron, Enzinger, Chris, Omran, Setareh Salehi, Sheth, Sunil A., Siddiqui, Adnan H., Starosciak, Amy K., Tarlov, Nicholas E., Taylor, Robert A., Wang, Michael J., Wolfe, Jared, Wong, Ka-Ho, Le, Huynh Vu, Gattringer, Thomas, Nguyen, Quy Viet, Pham, Thong Nhu, Nguyen, Trung Thanh, Phan, Hoang Thi, Ton, Mai Duy, Fischer, Urs, Michel, Patrik, Strambo, Davide, Martins, Sheila O., Zaidat, Osama O., Rahman, Aminur, Nogueira, Raul G., Bonnet, Thomas, Ligot, Noemie, De Raedt, Sylvie, Lemmens, Robin, Vanacker, Peter, Vandervorst, Fenne, Conforto, Adriana Bastos, Hidalgo, Raquel C.T., de Oliveira Neves, Luciana, Martins, Rodrigo Targa, Mora Cuervo, Daissy Liliana, Rebello, Leticia C., Santiago, Igor Bessa, Lameirinhas da Silva, Isabelle, Sakelarova, Teodora, Kalpachki, Rosen, Alexiev, Filip, Catanese, Luciana, Cora, Elena Adela, Goyal, Mayank, Hill, Michael D., Kelly, Michael E., Khosravani, Houman, Lavoie, Pascale, Peeling, Lissa, Pikula, Aleksandra, Rivera, Rodrigo, Chen, Hui-Sheng, Chen, Yimin, Huo, Xiaochuan, Miao, Zhongrong, Yang, Shuiquan, Bedekovic, Marina Roje, Bralic, Marina, Budincevic, Hrvoje, Corredor-Quintero, Angel Basilio, Lara-Sarabia, Osvaldo E., Cabal, Martin, Tenora, Dusan, Fibrich, Petr, Herzig, Roman, Hlaváčová, Helena, Hrabanovska, Emanuela, Hlinovsky, David, Jurak, Lubomir, Kadlcikova, Jana, Karpowicz, Igor, Klecka, Lukas, Kovar, Martin, Lauer, David, Neumann, Jiri, Palouskova, Hana, Reiser, Martin, Rekova, Petra, Rohan, Vladimir, Skoda, Ondrej, Škorňa, Miroslav, Sobotková, Lenka, Sramek, Martin, Zakova, Lenka, Christensen, Hanne, Drenck, Nicolas, and Repositório da Universidade de Lisboa

Abstract

© 2022 American Academy of Neurology, Background and objectives: Declines in stroke admission, IV thrombolysis (IVT), and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the effect of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), IVT, and mechanical thrombectomy over a 1-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020). Methods: We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, IVT treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases. Results: There were 148,895 stroke admissions in the 1 year immediately before compared with 138,453 admissions during the 1-year pandemic, representing a 7% decline (95% CI [95% CI 7.1-6.9]; p < 0.0001). ICH volumes declined from 29,585 to 28,156 (4.8% [5.1-4.6]; p < 0.0001) and IVT volume from 24,584 to 23,077 (6.1% [6.4-5.8]; p < 0.0001). Larger declines were observed at high-volume compared with low-volume centers (all p < 0.0001). There was no significant change in mechanical thrombectomy volumes (0.7% [0.6-0.9]; p = 0.49). Stroke was diagnosed in 1.3% [1.31-1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82-2.97], 5,656/195,539) of all stroke hospitalizations. Discussion: There was a global decline and shift to lower-volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared with the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year.

Published
2023

25. To Study the Predictive Value of Umbilical Cord Blood Bilirubin Levels Term Neonates as Marker of Neonatal Hyperbilirubinemia

Author

Jonnala Umesh and Jillela Mahesh Reddy

Subjects
medicine.medical_specialty, Cord, Receiver operating characteristic, Bilirubin, business.industry, Jaundice, Gastroenterology, Predictive value, Umbilical cord, chemistry.chemical_compound, medicine.anatomical_structure, chemistry, Internal medicine, medicine, Bilirubin levels, medicine.symptom, Prospective cohort study, business
Abstract

Background: Infants who are clinically jaundiced in the first few days are more likely to develop hyperbilirubinemia. Hyperbilirubinemia is the most common medical problem in newborn infants. It is the most important cause for hospital readmissions during the early neonatal period and also the cause for neonatal morbidity. Objective: In this prospective study we are going to evaluate the predictive value of cord bilirubin level for identifying term neonates for subsequent hyperbilirubinemia. Materials and Methods: Cord bilirubin levels at birth and subsequently serum bilirubin levels at 72 h were assessed in 291 neonates. The cutoff value was estimated beyond which there was significant hyperbilirubinemia. Statistics employed include quantitative data which is expressed in terms of mean and SD and qualitative in terms of proportions and receiver operator characteristic curve used to find cut-off value and to find sensitivity and specificity. Results: The cutoff value of cord bilirubin >2 mg/dl had sensitivity and specificity of 77.97 and 56.90%, respectively, with positive predictive value of 31.51 and negative predictive value of 91.03% for subsequent hyperbilirubinemia. Conclusion: To decreases the significant burden of untreated severe neonatal jaundice, cord serum bilirubin can be used as a screening tool to identify the neonatal jaundice in term and this prediction of neonatal hyperbilirubinemia has widespread implication especially in our country where there are limited resources. Keywords: Hyperbilirubinemia, Neonatal morbidity, cord serum bilirubin.

Published
2021

26. To Study the Serum Iron and Vitamin B12 Deficiency in Children in Different Communities in the South Indian State of Telangana: A Cross Sectional Study

Author

Jillela Mahesh Reddy and Jonnala Umesh

Subjects
education.field_of_study, medicine.diagnostic_test, Cross-sectional study, business.industry, Population, Ethnic group, Developing country, Serum iron, medicine, Observational study, Vitamin B12, education, business, Socioeconomic status, Demography
Abstract

Background: National Family Health Survey (NFHS-4 2015-2016) documented the prevalence of anaemia as overall more than three-quarters (76 percent) of children. Anaemia is the most common Haematological disease of the paediatric age group. Anaemia is the highest prevalence in developing countries. The population differences in the prevalence of anaemia are explained by environmental factors affecting nutrition, chief among these are economic status, ethnic customs & geographic considerations. Furthermore, there is very limited information on prevalence of Iron and B12 deficiencies among children belonging to different communities with culturally defined eating habits. In the present study carried out to compare the Serum Iron & Vitamin B12 in children of different communities in the South Indian state of Telangana. Material & Methods: In this population based cross sectional observational study was conducted on the department of paediatrics in the Chalmeda Anandrao Institute of Medical Sciences, Karimnagar, Telangana, during the period from 1st January 2020 to till reached the sample size. The study was conducted with the approval from the institutional review and ethical committees. In this study includes children were of the age between 5 to 18 years with the 3 different communities like, Hindu, Muslim & others community. Results: In the above table we shows that the Others community of age is 12.85 ± 2.65 years, Hindu community of age is 12.76 ± 3.46 years & Muslim community of age is 14.96 ± 2.00 years. In our study, the prevalence of Serum Iron was found to be 21.7% (26 out of 120) & prevalence of Vitamin B12 was found to be 50.0% (60 out of 120). Conclusion: The overall prevalence of anaemia (low Haemoglobin) was found to be 43.33%. There was no significant difference between the prevalence of anaemia in 3 different communities. Keywords: Serum Iron, Vitamin B12, Anaemia, Deficiency.

Published
2021

28. Optical analysis of RE

Author

Guduru, Moulika, Sannapureddy, Sailaja, Jillela, Santhosh Vijitha, Putluru, Bayapu Reddy, Kondala, Shanthi Latha, Gasthi, Venkata Chalapathi, and Busireddy, Sudhakar Reddy

Subjects
Ions, Calcium, Glass, Lithium, Zinc Oxide
Abstract

Calcium boro fluoro zinc phosphate glasses modified using alkali oxide and doped with Nd

Published
2022

32. Modeling Seasonal Variation in Urban Weather in Sub-Tropical Region of Delhi

Author

Praveen K. Thakur, A. Senthil Kumar, Jai Shankar Gummapu, Pushplata, Payel Ghosh Dastidar, Allaka Lalitha, Jillela Malleswara Rao, and Kshama Gupta

Subjects
Geography, Planning and Development, 0211 other engineering and technologies, Mesoscale meteorology, 02 engineering and technology, Land cover, Seasonality, Monsoon, medicine.disease, Numerical weather prediction, Wind speed, Climatology, Weather Research and Forecasting Model, Earth and Planetary Sciences (miscellaneous), medicine, Environmental science, Climate model, 021101 geological & geomatics engineering
Abstract

Complexity and heterogeneity of urban areas lead to difficulty in urban weather simulations and climate modeling. Diversity and size of urban areas necessitate to downscale global climate models to urban scale (~ hundreds of meters) and to enhance urban parameterization in the models to realistically simulate urban weather conditions. Hence, in this study, a methodology has been developed to generate multi-class urban land use land cover (LULC) by employing Resourcesat-2 LISS IV data. Weather Research and Forecast (WRF) model which is also a mesoscale numerical weather prediction and regional climate model was utilized to downscale the meteorological parameters up to 0.5 km grid resolution. Multi-class urban LULC prepared with improved urban parameters and updated Land Surface Parameters (LSPs) was ingested in model for Delhi to evaluate the model performance in three dominant seasons, i.e., summer, monsoon and winter. Evaluation of model performance with ground observation data revealed that multi-class urban LULC along with updated LSPs provided improved RMSE values of 2.31° C, 1.79 m/s and 0.94 mbar as compared to ingestion of multi-class urban LULC only (RMSE values of 3.42° C, 3.72 m/s and 1.58 mbar) for temperature at 2 m, wind speed and surface pressure, respectively. Temperature is found to be highest in summer season (38.58° C) and lowest in winter season while relative humidity is highest in monsoon season (~ 88%) and lowest in summer season (~ 30%). The study highlights the importance of ingestion of updated LSPs along with updated multi-class urban LULC for enhanced model performance.

Published
2020

33. Hyperspectral and multispectral data fusion using fast discrete curvelet transform for urban surface material characterization

Author

Asfa Siddiqui, Jillela Malleswara Rao, Sandeep Maithani, and Pramod Kumar

Subjects
Urban surface, Image fusion, Fusion, 010504 meteorology & atmospheric sciences, Computer science, Geography, Planning and Development, Multispectral image, 0211 other engineering and technologies, Hyperspectral imaging, 02 engineering and technology, Sensor fusion, 01 natural sciences, Characterization (materials science), Curvelet, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Water Science and Technology, Remote sensing
Abstract

The objective of the present study is to analyze the quality of hyperspectral data fusion using low spatial hyperspectral (LSH) Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRI...

Published
2020

34. Health insurance schemes: A cross-sectional study on levels of awareness by patients attending a tertiary care hospital of coastal south India

Author

Ashfiya Afrath Mariyam, Priya Rathi, Basavaraj Ulligaddi, Bhaskaran Unnikrishnan, Abhinav Pandey, C. Praveen Kumar, and Jillela Sairama Gayatri Saran

Subjects
medicine.medical_specialty, Leadership and Management, Cross-sectional study, business.industry, Health Policy, Indian population, Tertiary care hospital, Medical expenditure, Level of consciousness, Informed consent, Family medicine, medicine, Health insurance, business
Abstract

Background: Health insurance (HI) is one of the ways by which Out-Of-Pocket Expenditure can be reduced. However, only 15% of the Indian population purchased some kind of HI. Thus, we aimed to study the awareness, enrollment, and reasons for non-enrollment of HI among patients attending public and private hospitals. Materials and methods: A hospital-based cross-sectional study was conducted among 403 patients from both public and private hospitals attached to a medical college in Mangalore. Patients were interviewed using a pre-tested, semi-structured questionnaire after obtaining a written informed consent. The data were entered and analyzed using SPSS 25. Results: The study shows that 74.4% of the patients were aware of HI. The main sources of information were friends and Newspaper.71% had purchased a HI. The foremost reason to purchase a HI was to cover medical expenditure (84.6%), chiefly the surgical expenses. The most cited reason for non-enrollment in HI schemes was lack of awareness. Conclusion: The awareness regarding HI was found to be high. However, not all who were aware had a HI.

Published
2019

35. Investigation of the inhibition of eight major human cytochrome P450 isozymes by a probe substrate cocktail in vitro with emphasis on CYP2E1

Author

Lakshman Rajagopalan, Srinivas Lenkalapelly, Guru R. Valicherla, Pratima Srivastava, Bhupathi Jillela, Amrut Mishra, and Femi M Francis

Subjects
Pharmacology, Chromatography, CYP3A4, CYP2B6, Chemistry, Health, Toxicology and Mutagenesis, CYP1A2, General Medicine, Toxicology, Sulfaphenazole, 030226 pharmacology & pharmacy, Biochemistry, Isozyme, 03 medical and health sciences, 0302 clinical medicine, Liquid chromatography–mass spectrometry, 030220 oncology & carcinogenesis, Chlorzoxazone, medicine, Microsome, medicine.drug
Abstract

1. A protocol has been developed and validated for the high-throughput screening of eight major human cytochrome P450 (CYP) isozymes inhibition (CYP 1A2, 2C9, 2C19, 2D6, 3A4, 2B6, 2C8 and 2E1) using an in vitro probe cocktail containing eight substrates by overcoming the unfavorable effect of assay conditions on CYP2E1 inhibition data. 2. The cocktail consisting of selective probe substrates like tacrine (CYP1A2), diclofenac (CYP2C9), S-mephenytoin (CYP2C19), dextromethorphan (CYP2D6), midazolam (CYP3A4), bupropion (CYP2B6), paclitaxel (CYP2C8) and chlorzoxazone (CYP2E1) was incubated with human liver microsomes. 3. The method was investigated by incubating well-known CYP inhibitors {alphanaphthoflavone (CYP1A2), sulfaphenazole (CYP2C9), N-3-benzylnirvanol (CYP2C19), quinidine (CYP2D6), ketoconazole (CYP3A4), ticlopidine (CYP2B6), quercetin (CYP2C8) and 4-methylpyrazole (CYP2E1)} with the substrate cocktail. A fast gradient liquid chromatography tandem mass spectrometry (LC-MS/MS) was used for this study. 4. The IC50 values determined for typical CYP inhibitors were reproducible and consistent with those in the literature. DMSO has significant effect and itself inhibits CYP2E1. DMSO should not exceed 0.1% for the determination of reliable CYP2E1 inhibition profile. This cocktail assay offers an efficient and robust method to determine the CYP450 isoforms inhibition profiles of large numbers of compounds in a quick turnaround time.

Published
2019

38. Optical analysis of RE3+ (RE = Nd or Er):B2O3–P2O5–CaF2–ZnO–(Li2O/Na2O/K2O) glasses.

Author

Moulika, Guduru, Sailaja, Sannapureddy, Santhosh Vijitha, Jillela, Bayapu Reddy, Putluru, Shanthi Latha, Kondala, Venkata Chalapathi, Gasthi, and Sudhakar Reddy, Busireddy

Abstract

Calcium boro fluoro zinc phosphate glasses modified using alkali oxide and doped with Nd3+ and Er3+ ions with the chemical composition of 69.5 (B2O3) + 10 (P2O5) + 10 (CaF2) + 5 (ZnO) + 5 (Na2O/Li2O/K2O) + 0.5 (Er2O3/Nd2O3) were prepared using a conventional melt quenching technique. The results of X‐ray diffraction patterns indicated the amorphous nature of all the prepared glasses. The visible–near‐infrared red (NIR) absorption spectra of these glasses were analyzed systematically. The NIR emission spectra of Er3+ and Nd3+:calcium boro fluoro zinc phosphate glasses showed prominent emission bands at 1536 nm (4I13/2→4I15/2) and 1069 nm (4F3/2→4I11/2) respectively with λexci = 514.5 nm (Ar+ laser) as the excitation source. [ABSTRACT FROM AUTHOR]

Published
2022
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41. An Impact Study on Training Programme on Integrated Pest Management

Author

Gurram Ranjitha, Jillela Teza, and A. Veeraiah

Subjects
0106 biological sciences, Integrated pest management, 010602 entomology, 050204 development studies, 0502 economics and business, 05 social sciences, Impact study, Business, 01 natural sciences, Training programme, Environmental planning
Published
2018

45. Hyperspectral and multispectral data fusion using fast discrete curvelet transform for urban surface material characterization.

Author

Malleswara Rao, Jillela, Siddiqui, Asfa, Maithani, Sandeep, and Kumar, Pramod

Subjects
*CURVELET transforms, *SURFACE analysis, *MULTISENSOR data fusion, *SURFACES (Technology), *INFRARED imaging, *CERAMIC tiles, *SANDY soils
Abstract

The objective of the present study is to analyze the quality of hyperspectral data fusion using low spatial hyperspectral (LSH) Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) 8 m data and high spatial multispectral (HSM) WorldView-3 image at 1.24 m remote sensing images with spectral unmixing technique. The resultant HSH data shows new prospects for urban surface material characterization with spectrally distinct classes. The spatial resolution of LSH is enhanced by injecting the high-frequency details from the corresponding HSM bands in fast discrete curvelet transform domain. The image fusion-based products' quality has been analyzed by endmembers extraction and fractional maps generated using Piecewise Convex Multiple-Model Endmember Detection (PCOMMEND) method. Experimental results showed that the fusion has improved the spatial as well as spectral separability to extract the endmembers, particularly for the urban surface materials like the combination of water and asphalt, and bare soil and roof tiles. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
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48. Techniques for Ocular Biometric Recognition Under Non-ideal Conditions

Author

Raghavender Jillela and Arun Ross

Subjects
Engineering, Biometrics, business.industry, fungi, Motion blur, Iris recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Sparse approximation, Facial recognition system, ComputingMethodologies_PATTERNRECOGNITION, medicine.anatomical_structure, Face (geometry), medicine, Three-dimensional face recognition, Computer vision, Artificial intelligence, Iris (anatomy), business
Abstract

The use of the ocular region as a biometric cue has gained considerable traction due to recent advances in automated iris recognition. However, a multitude of factors can negatively impact ocular recognition performance under unconstrained conditions (e.g., non-uniform illumination, occlusions, motion blur, image resolution, etc.). This dissertation develops techniques to perform iris and ocular recognition under challenging conditions. The first contribution is an image-level fusion scheme to improve iris recognition performance in low-resolution videos. Information fusion is facilitated by the use of Principal Components Transform (PCT), thereby requiring modest computational efforts. The proposed approach provides improved recognition accuracy when low-resolution iris images are compared against high-resolution iris images. The second contribution is a study demonstrating the effectiveness of the ocular region in improving face recognition under plastic surgery. A score-level fusion approach that combines information from the face and ocular regions is proposed. The proposed approach, unlike other previous methods in this application, is not learning-based, and has modest computational requirements while resulting in better recognition performance. The third contribution is a study on matching ocular regions extracted from RGB face images against that of near-infrared iris images. Face and iris images are typically acquired using sensors operating in visible and near-infrared wavelengths of light, respectively. To this end, a sparse representation approach which generates a joint dictionary from corresponding pairs of face and iris images is designed. The proposed joint dictionary approach is observed to outperform classical ocular recognition techniques. In summary, the techniques presented in this dissertation can be used to improve iris and ocular recognition in practical, unconstrained environments.

Published
2019

49. Investigation of the inhibition of eight major human cytochrome P450 isozymes by a probe substrate cocktail

Author

Guru R, Valicherla, Amrut, Mishra, Srinivas, Lenkalapelly, Bhupathi, Jillela, Femi M, Francis, Lakshman, Rajagopalan, and Pratima, Srivastava

Subjects
Tandem Mass Spectrometry, Drug Evaluation, Preclinical, Microsomes, Liver, Cytochrome P-450 Enzyme Inhibitors, Humans, Reproducibility of Results, Dimethyl Sulfoxide, Cytochrome P450 Family 3, Cytochrome P450 Family 2, Chromatography, Liquid, Substrate Specificity
Abstract

1. A protocol has been developed and validated for the high-throughput screening of eight major human cytochrome P450 (CYP) isozymes inhibition (CYP 1A2, 2C9, 2C19, 2D6, 3A4, 2B6, 2C8 and 2E1) using an

Published
2019

50. A study on predictors of low birth weight

Author

Jillela Mahesh Reddy and Sasi Priya Aravalli

Subjects
Low birth weight, medicine.medical_specialty, business.industry, Obstetrics, Medicine, medicine.symptom, business
Abstract

Background: purpose of this study was to determine prevalence of maternal and social risk factors of low birth weight. The purpose of this study is to prevalence of maternal and social risk factors of low birth weight.Methods: The cross-sectional and comparative study was carried out by reviewing medical records of newborn delivered for one year in 250 newborn. Birth weight was categorized into two as low birth weight (birth weight

Published
2021

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