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211 results on '"Khadanga, Sagar"'

1. Health-care-associated bloodstream and urinary tract infections in a network of hospitals in India: a multicentre, hospital-based, prospective surveillance study

Author

Malhotra, Rajesh, Katoch, Omika, Katyal, Sonal, Khurana, Surbhi, Kumar, Subodh, Agrawal, Richa, Dev Soni, Kapil, Sagar, Sushma, Wig, Naveet, Garg, Pramod, Kapil, Arti, Lodha, Rakesh, Sahu, Manoj, Misra, M.C., Lamba, Mamta, Jain, Shristi, Paul, Hema, Sarojini Michael, Joy, Kumar Bhatia, Pradeep, Singh, Kuldeep, Gupta, Neeraj, Khera, Daisy, Himanshu, D, Verma, Sheetal, Gupta, Prashant, Kumar, Mala, Pervez Khan, Mohammed, Gupta, Sarika, Kalwaje Eshwara, Vandana, Varma, Muralidhar, Attal, Ruchita, Sudhaharan, Sukanya, Goel, Neeraj, Saigal, Saurabh, Khadanga, Sagar, Gupta, Ayush, Thirunarayan, M.A., Sethuraman, Nandini, Roy, Ujjaini, Jyoti Raj, Hirak, D'Souza, Desma, Chandy, Mammen, Mukherjee, Sudipta, Kumar Roy, Manas, Goel, Gaurav, Tripathy, Swagata, Misra, Satyajeet, Dey, Anupam, Misra, Tushar, Ranjan Das, Rashmi, Bashir, Gulnaz, Nazir, Shaista, Ranjana Devi, Khuraijam, Chaoba Singh, Langpoklakpam, Bhargava, Anudita, Gaikwad, Ujjwala, Vaghela, Geeta, Sukharamwala, Tanvi, Ch. Phukan, Anil, Lyngdoh, Clarissa, Saksena, Rushika, Sharma, Rajeev, Velayudhan, Anoop, Mathur, Purva, Malpiedi, Paul, Walia, Kamini, Srikantiah, Padmini, Gupta, Sunil, Lohiya, Ayush, Chakrabarti, Arunaloke, Ray, Pallab, Biswal, Manisha, Taneja, Neelam, Rupali, Priscilla, Balaji, Veeraraghavan, Rodrigues, Camilla, Lakshmi Nag, Vijaya, Tak, Vibhor, Venkatesh, Vimala, Mukhopadhyay, Chiranjay, Deotale, Vijayshri, Padmaja, Kanne, Wattal, Chand, Bhattacharya, Sanjay, Karuna, Tadepalli, Behera, Bijayini, Singh, Sanjeev, Nath, Reema, Ray, Raja, Baveja, Sujata, Fomda, Bashir A, Sulochana Devi, Khumanthem, Das, Padma, Khandelwal, Neeta, Verma, Prachi, Bhattacharyya, Prithwis, Gaind, Rajni, Kapoor, Lata, Gupta, Neil, Sharma, Aditya, VanderEnde, Daniel, Siromany, Valan, Laserson, Kayla, and Guleria, Randeep

Published
2022
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2. Electrodiagnostic assessment of autonomic nervous system in diabetic autonomic neuropathy: Case series in 72 diabetes patients.

Author

Dube, Suchitra, Hulke, Sandip M., Wakode, Santosh L., and Khadanga, Sagar

Subjects
AUTONOMIC nervous system, HEART beat, DIABETIC neuropathies, TYPE 2 diabetes, DIABETES complications
Abstract

ABSTRACT: Diabetic autonomic neuropathy is one of the most common complications of diabetes which is asymptomatic in early stage. Hence HRV and AFT are performed to detect and prevent its progression to advanced stages. To discuss the HRV and AFT findings in diabetic patients. An observational study was conducted in Physiology department of AIIMS, Bhopal in collaboration with the Medicine department of the institute. Seventy-two diagnosed Type-2 Diabetes mellitus patients were examined using POWER LAB AND LAB CHART 8 BY AD INSTRUMENT: For recording heart rate variability (HRV) and Ewing's battery of tests: For Autonomic function testing. Microsoft Excel was utilized for data compilation and for result analysis. In the present study, based on HRV parameters sympathetic involvement was seen in 39% while parasympathetic involvement was seen in 15% of patients. Balanced state was observed in 46% of the patients. Based on AFT battery, normal early involvement was observed in 62% of the patients while definite and severe involvement was observed in 14 and 24%, respectively. Diabetic autonomic neuropathy was observed in 72% of the patients based on HRV and AFT testing. In severe involvement, orthostatic hypotension was seen. Severe involvement was observed in 24% of the patients in present study. Involvement of the vagal parasympathetic component of ANS was obvious evidenced by increase in resting heart rate, decrease in Valsalva ratio, E/I index, and standing ratio. Diabetic autonomic neuropathy was observed in majority of patients with normal early involvement seen predominantly and though majority of them were in balanced state, sympathetic involvement was also seen at large. HRV studies depicted a decrease of total power, decreased low frequency (LF) and high frequency (HF) with LF/HF and SD1/SD2 ratios depicting sympathetic involvement. [ABSTRACT FROM AUTHOR]

Published
2025
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3. Remdesivir and three other drugs for hospitalised patients with COVID-19: final results of the WHO Solidarity randomised trial and updated meta-analyses

Author

Pan, Hongchao, Peto, Richard, Henao Restrepo, Ana Maria, Preziosi, Marie-Pierre, Sathiyamoorthy, Vasee, Karim, Quarraisha Abdool, Alejandria, Marissa, Hernàndez García, César, Kieny, Marie-Paule, Malekzadeh, Reza, Murthy, Srinivas, Reddy, K. Srinath, Periago, Mirta Roses, Hanna, Pierre Abi, Abutidze, Akaki, Ader, Florence, Al-Bader, Abdullah, Alhasawi, Almonther, Allum, Emma, Al Mawali, Adhra, Alotaibi, Athari, Alvarez- Moreno, Carlos, Appadoo, Sheila, Arts, Derk, Asiri, Abdullah, Aukrust, Pål, Barratt-Due, Andreas, Genetu Bayih, Abebe, Beaumont, Helena, Bellani, Samir, Benassi, Virginia, Bhargava, Balram, Branca, Mattia, Cappel-Porter, Heike, Cerrato, Nery, Cheick Haidara, Fadima, Chow, Ting Soo, Como, Nadia, Eustace, Joe, Gabunia, Tamar, García, Patricia, Godbole, Sheela, Gotuzzo, Eduardo, Griskevicius, Laimonas, Hamra, Rasha, Hassan, Mariam, Hassany, Mohamed, Hutton, David, Irmansyah, Irmansyah, Jancoriene, Ligita, Khamis, Faryal, Kirwan, Jana, Kumar, Suresh, Lennon, Peter, Lopardo, Gustavo, Lydon, Patrick, Magrini, Nicola, Manevska, Suzana, Manuel, Oriol, McGinty, Sybil, Medina, Marco, Mesa Rubio, Maria Lucia, Miranda Montoya, Maria Consuelo, Nel, Jeremy, Nunes, Estevao, Perola, Markus, Portoles, Antonio, Rasmin, Menaldi, Raza, Aun, Rees, Helen, Reges, Paula, Rogers, Chris, Salami, Kolawole, Salvadori, Marina, Sauermann, Mamatha, Sinani, Narvina, Sow, Samba, Sterne, Jonathan AC, Stevanovikj, Milena, Tacconelli, Evelina, Tavares Maltez, Fernando Manuel, Teferi, Mekonnen, Tikkinen, Kari, Trelle, Sven, Tsertsvadze, Tengiz, Zaid, Hala, Røttingen, John-Arne, Swaminathan, Soumya, Ryan, Michael, Gjermeni, Nevila, Meta, Esmeralda, Aguila, Damian, Alonso, Ignacio, Altamirano, Marcos, Alvarez, María, Alzola, Rodrigo, Arce, Veronica, Arribillaga, Patricia, Avila, Rafael, Balbuena, Juan, Barcelona, Laura, Barletta, José, Benedetti, María, Berdiñas, Verónica, Burgui, Julieta, Caimi, Sabrina, Carrillo, Juan, Carrizo, Juan, Castelli, Juan, Cazaux, Alexis, Cervellino, Flavia, Chalco, Angelo, Chediack, Viviana, Cunto, Eleonora, D'Amico, Nicolàs, de Vedia, Lautaro, Delgado, Carolina, Di Pilla, Debora, Díaz, Miguel, Díaz Aguiar, Pablo, Domínguez, Cecilia, Ellero, Leonor, Farina, Javier, Fernàndez, José, Ferreyra, Roxana, Filippi, María, Fogar, Carolina, Frare, Pablo, Giudiche, Celeste, Golikow, Mariana, Gomez, Maria Georgina, Hermida, Laura, Hurtado, Mariano, Jacobo, Mariela, Jaume, Martin, Laplume, Diego, Lescano, María, Lista, Nicolàs, Loiacono, Flavia, López, Ana Belen, Losso, Marcelo, Luna, Cecilia, Lupo, Sergio, Marianelli, Leonardo, Martin, Anabella, Masciottra, Florencia, Mykietiuk, Analía, Orellano, Lorena, Pachioli, Valeria, Padilla, María José, Pallavicini, Cecilia, Patroso, Jazmin, Perez Blanco, Luz, Presas, Jose Louis, Provenzano, Matias, Lavera, Lorena, Reichert, Viviana, Riveros, Florencia, Rodríguez, Alejandra, Rolon, María José, Salvay, Carolina, Simonetta, María, Sisto, Alicia, Themines, Sandra, Tito, Fernando, Toibaro, Javier, Torales, Graciela, Verón, Luciano, Vizzotti, Carla, Egle, Alexander, Greil, Richard, Joannidis, Michael, Altdorfer, Antoine, Belkhir, Leila, Fraipont, Vincent, Hites, Maya, Arruda, Erico, Breda, Giovanni, Colussi, Arthur, Corradi, Miran, Croda, Julio, Duani, Helena, João, Esaú, Machado, Elizabeth, Mello, Fernanda, Miranda Filho, Demócrito, Monteiro, Poliana, Nunes, Ceuci, Pereira Junior, Luiz Carlos, Pinto, Gustavo, Raboni, Sonia, Ramos, Marcelo, Ruffing, Leonardo, Santos, Valdilea, Souza, Tamara, Medeiros, Melissa, Schwarzbold, Alexandre, Ali, Karim, Azher, Tanweer, Bellemare, David, Binnie, Alexandra, Borgia, Sergio, Cavayas, Yiorgos Alexandros, Chagnon, Nicholas, Cheng, Matthew, Cloutier, Eve, Conly, John, Costiniuk, Cecilia, Daneman, Nick, Douglas, James, Downey, Catarina, Duan, Erick, Durand, Medeline, English, Shane, Farjou, George, Fera, Evadiki, Fontela, Patricia, Fowler, Rob, Fralick, Mike, Gamble, David Gregory, Geagea, Anna, Grant, Jennifer, Harrison, Luke, Havey, Thomas, Hoang, Holly, Kelly, Lauren, Keynan, Yoav, Khwaja, Kosar, Klein, Marina, Kolan, Christophe, Kronfli, Nadine, Lamontagne, Francois, Lee, Nelson, Lee, Todd, Lim, Rachel, Lostun, Alexandra, MacIntyre, Erika, Malhamé, Isabelle, Martin-Carrier, Francois, McGuinty, Marlee, Munan, Matthew, O'Neil, Conar, Ovakim, Daniel, Papenburg, Jesse, Parhar, Ken, Parvathy, SeemaNair, Perez-Patrigeon, Santiago, Rishu, Asgar, Rushton, Moira, Scherr, Kim, Schwartz, Kevin, Semret, Makeda, Silverman, Micahel, Singh, Ameeta, Sligl, Wendy, Smith, Stephanie, Somayaji, Ranjani, Tan, Darrell, Tran, Tuong-Vi, Tremblay, Alain, Tsang, Jennifer, Turgeon, Alexis, Vakil, Erik, Weatherald, Jason, Yansouni, Cedric, Zarychanski, Ryan, Aristizabal, Claudia, Bravo, Juan, Caicedo, Monica, Chacón, Julio, Garzón, Diego, Guevara, Fredy, Lozano-Gonzàlez, Silvia, Macareno, Hugo, Montañez-Ayala, Anita, Oñate, Jose, Rojas-Gambasica, Jose, Rosso, Fernando, Saavedra, Carlos, Valderrama, Sandra, Vàquiro-Herrera, Eliana, Varón-Vega, Fabio, Zuluaga, Ivan, Abdel Baki, Amin, Abdelbary, Akram, Abdel-Razek, Wael, Amin, Wagdi, Asem, Noha, Elassal, Gehan, Elshesheny, Marwa, Fathy, Mohamed, Fathy, Naglaa, Fayed, Notaila, Hammam, Ahmed, Hassany, Sahar, Ibrahim, Hamdy, Kamal, Ehab, Masoud, Hossam, Mohamed, Maryam, Mohamed Gouda, Abdullah, Moustafa, Ehab, Okasha, Shaimaa, Rafik, Ahmed, Said, Ahmed, Sedky, Asmaa, Solyman Kabil, Mohamed, Tarek, Sara, Tharwat, Ahmed, Zaky, Samy, Abegaz, Emawayish Tesema, Bekele, Zelalem Mekonnen, Asfaw, Filmona Mekuria, Tegegne, Netsanet Aragaw, Teklemariam, Miheret Fikre, Nigusse, Frehiwot Tamiru, Achalu, Daniel Legesse, Weldegergs, Shewit Tesfagabr, Huluka, Dawit Kebede, Tereda, Addisu Birhanu, Ala-Kokko, Tero, Delany, Jutta, Ekroos, Heikki, Hankkio, Riina, Haukipää, Mia, Hetemäki, Iivo, Holma, Pia, Holmberg, Ville, Horstia, Saana, Jalkanen, Ville, Jämsänen, Toni, Järventie, Juuso, Järvinen, Petrus, Kalliala, Ilkka, Kauma, Heikki, Kilpeläinen, Tuomas, Kreivi, Hanna-Riikka, Kuitunen, Ilari, Lamminmäki, Satu, Mäkinen, Laura, Mäntylä, Jarkko, Mattila, Tiina, Myllärniemi, Marjukka, Niskanen, Joni, Nykänen, Taina, Nyqvist, Miro, Paajanen, Juuso, Partanen, Terhi, Patovirta, Riitta-Liisa, Paukkeri, Erja-Leena, Puusti, Emmi, Renner, Andreas, Reponen, Emma, Risku, Sari, Rosberg, Tuomas, Rutanen, Jarno, Säilä, Petrus, Salonen, Päivi, Sinisalo, Marjatta, Sivenius, Katariina, Tuominen, Susanna, Aboab, Jerone, Alfaiate, Toni, Andrejak, Claire, Andreu, Pascal, Belhadi, Drifa, Benezit, Francois, Botelho-Nevers, Elisabeth, Bouadma, Lila, Bougon, David, Bouiller, Kevin, Bounes, Fanny, Boyer, Alexandre, Bruel, Cédric, Buffet, Alexandre, Burdet, Charles, Cazanave, Charles, Chabertier, Cyrille, Clere-Jehl, Rapahel, Costagliola, Dominique, Courjon, Johan-Victor, Crockett, Flora, Danion, Francois, Dechanet, Aline, Dellamonica, Jean, Delmas, Christelle, Diallo, Alpha, Djossou, Felix, Dubost, Clement, Dupont, Axelle, Epaulard, Olivier, Faure, Emmmanuel, Faure, Karine, Fayol, Antoine, Figueiredo, Samy, Fougerou, Claire, Gaborit, Benjamin, Gaci, Rostane, Gagneux-Brunon, Amandine, Gallien, Sebastien, Garot, D, Goehringer, Francois, Gruson, Didier, Hinschberger, Olivier, Hulot, Jean-Sebastien, Jaureguiberry, Stephane, Jean-Michel, Vanessa, Kerneis, Solen, Kimmoun, Antoine, Klouche, Kada, Lachatre, Marie, Lacombe, Karine, Laine, Fabrice, Lanoix, Jean Philippe, Laribi, Samira, Launay, Odile, Laviolle, Bruno, Le Moing, Vincent, Le Pavec, Jerome, Lebeaux, David, Leroy, Sylvie, Lescure, Xavier, Livrozet, Marine, Makinson, Alain, Malvy, Denis, Marquette, Charles-Hugo, Martin-Blondel, Guillaume, Mayaux, Julien, Mekontso Dessap, Armand, Mentre, France, Mercier, Noemie, Meziani, Ferhat, Molina, Jean Michel, Mootien, Yoganaden, Mourvillier, Bruno, Navellou, Jean Christoph, Noret, M, Peiffer- Smadja, Nathan, Peytavin, Gilles, Pialoux, Gilles, Pilmis, Benoît, Piroth, Lionel, Poindron, Vincent, Poissy, Julien, Pourcher, Valerie, Quenot, Jean Pierre, Raffi, Francois, Reignier, Jean, Richard, Jean Christoph, Robert, Céline, Saillard, Juliette, Sayre, Naomi, Senneville, Eric, Stefan, Francois, Tellier, Marie Capucine, Terzi, Nicolas, Textoris, Julien, Thiery, Guillame, Timsit, Jean Francois, Tolsma, Violaine, Tubiana, Sarah, Wallet, Florent, Yazdanpanah, Yazdan, Zerbib, Yoann, Aguilar, Carlos, Erazo, Laura, Fiallos, Angel, Figueroa, Rosbinda, Flores, Juan Jose, Melendez, Lesddyy, Moncada, Wendy, Abraham, Ooriapadickal Cherian, Acharya, Chetankumar, Aedula, Vinaya Sekhar, Aggarwal, Richa, Agrawal, Nishant, Agrawal, Umang, Agrawal, Abhishekh, Ahmad, Mohammad, Atal, Shubham, Babu, Avinash, Baidya, Dalim Kumar, Balachandran, Amith, Bangar, Rakhee, Bhadade, Rakesh, Bhandari, Sudhir, Bhapal, Meghavi, Bhardwaj, Pankaj, Bhati, Gaurav, Bhatia, Pradeep, Bhatt, Krishnakant, Bingi, Thrilok Chander, Borse, Rohidas, Buch, Vyom, Chand, Dipti, Chandwani, Ashish, Charan, Jaykaran, Chaudhari, Mayur, Chaudhari, Kirti, Chaudhary, Vipul, Chauhan, Nishant, Chikara, Gaurav, Daswani, Bharti, de Souza, Rosemarie, Desai, Chetna, Divakar, Balusamy, Divhare, Sujeet, Dorairajan, Suresh Kumar, Dutt, Naveen, Ethirajan, Therani Rajan, Gamit, Amit, Gamit, Sweta, Garg, Mahendra, Goenka, Ajay, Goenka, Aniket, Guleria, Randeep, Gupta, Paras, Gupta, Nivedita, Gupta, Madhur, Harde, Minal, Ingle, Vaibhav, Iyer, Shivkumar, Jamalapuram, Vaishnavi, Jayanthi, Rangarajan, Joshi, Rajnish, Kadam, Abhijeet, Kalakuntla, Hemanth, Kalikar, Mrunalini, Kalme, Sayali, Kamble, Suchit, Kant, Ravi, Kantharia, Bansari, Kashikar, Arundhati, Kavishvar, Abhay, Kayina, Choro Athipro, Kerkar, Pranali, Khadanga, Sagar, Khandare, Sagar, Kokate, Pranjali, Komathi, Jayavelu, Krishnan, Vijay, Krishnan, Jayasree, Krishnan, Sumitra, Kulur Mukhyaprana, Sudha, Kumarasamy, N, Mahavar, Sunil, Maitra, Souvik, Majumdar, Falguni, Malhotra, Supriya, Mamulwar, Megha, Malini, Padma, Marwah, Vikas, Maurya, Akhilesh, Mehta, Kedar, Mesipogu, Rajarao, Misra, Shobha, Mitra, Sajal, Mittal, Ankit, Mohan, Bharathi, Momin, Mohmmedirfan, Nag, Vijaya, Nagarajan, Ramakrishnan, Nagmani, Kammili, Narlawar, Uday, Natarajan, Gopalakrishnan, Nischal, Neeraj, Ogale, Dhananjay, Palat, Paltial, Panda, Prasan, Panda, Samiran, Pandya, Amee, Parate, Rohit, Paritekar, Arunita, Patel, Parvati, Patel, Chetna, Patel, Sunaina, Patel, Vitan, Patel, Deep, Patel, Harshad, Patil, Girish, Peter, Deepu, Prasad, Durga, Purohit, Vimlesh, Rabindrarajan, Ebenezer, Ranganathan, Lakshmiarasimhan, Rao, Tushara, Rao, Chakradhara, Rathod, Chirag, Raval, Devang, Ray, Avik, Reddy, Kamini, Rege, Sujata, Revathi, Ayyasamy, Roy, Dhara, Saigal, Saurabh, Sane, Suvarna, Sangale, Shashi, Seetharaman, Krishnamoorthy, Selvamuthu, Poongulali, Seshaiah, Kurada Venkata, Shadrach, Benhur, Shah, Jignesh, Shah, Sonal, Sharma, Swati, Sharma, Raman, Sharma, Shrikant, Singh, Krishna, Singh, Anil, Singh, Arjeet, Singhai, Abhishek, Soneja, Manish, Soni, Kapil Dev, Subhan, Thasneem banu, Subramaniam, Sudharshini, Sudarsanam, Thambu David, Sudarsi, Ravindra Kumar, Suleman, Dawood, Suthar, Nilay, Talati, Shriraj, Tambe, Murlidhar, Tejomurtula, Tilak, Tirupakuzhi Vijayaraghavan, Bharatkumar, Trikha, Anjan, Trivedi, Aarti, Udwadia, Zarir, Upadhyay, Kamlesh, Vasava, Ashwin, Vasudevan, Damodaran, Velayudham, Rajendran, Venkatasubramanian, Ramasubramanian, Verma, Mamta, Waghmare, Rakesh, Waikar, Anushka, Wig, Naveet, Afrilia, Annisa Rizky, Amin, Muhammad, Arlinda, Dona, Avrina, Rossa, Bang, Lois, Djaharuddin, Irawaty, Djojo, Aryan, Driyah, Sri Laning, Erastuti, Mila, Fajarwati, Tetra, Harsini, Harsini, Hartantri, Yovita, Herman, Deddy, Isbaniah, Fathiyah, Karyana, Muhammad, Kusuma, Indra, Mahmudji, Harli Amir, Medison, Irvan, Nugroho, Agung, Nurhayati, Nurhayati, Opitasari, Cicih, Pitoyo, Ceva Wicaksono, Pradana, Antonius Arditya, Raharjo, Sofyan Budi, Rahmaini, Ade, Risniati, Yenni, Riyanto, Bambang Sigit, Sajinadiyasa, I Gede Ketut, Sari, Flora Eka, Sitompul, Pompini Agustina, Soedarsono, Soedarsono, Somia, I Ketut Agus, Sugiri, Yani Jane, Sugiyono, Retna Indah, Susanto, Nugroho Harry, Syarif, Armaji Kamaludi, Yulianto, Aris, Afsharian, Mandana, Akhavi Mirab, Atefehsadat, Amini, Fatemeh, Amini, Mahnaz, Ansarin, Khalil, Baba Mahmoodi, Farhang, Baghaei, Parvaneh, Barazandeh, Fateme, Bayani, Masomeh, Dastan, Farzaneh, Ebrahimpour, Soheil, Eghtesad, Sareh, Fallahi, Mohammad Javad, Fallahpoor Golmaee, Fatemeh, Foroghi Ghomi, Seyed Yaser, Ghadir, Mohammad Reza, Gheitani, Mina, Ghiasvand, Fereshteh, Hafizi Lotfabadi, Saied, Hakamifard, Atousa, Hashemi Madani, Shima Sadat, Hormati, Ahmad, Hosseini, Hamed, Janbakhsh, Alireza, Javanian, Mostafa, Joukar, Farahnaz, Kamali, Alireza, Karampour, Amin, Khajavirad, Nasim, Khodabakhshi, Behnaz, Khodadadi, Javad, Khodashahi, Rozita, Kiani Majd, Somaieh, Mahfoozi, Lida, Mahmoodiyeh, Behnam, Mansour-Ghanaei, Fariborz, Mansouri, Feizollah, Mesgarpour, Bita, Mesri, Mehdi, Mikaeili, Haleh, Miladi, Ronak, Moghadami, Mohsen, Mohamadi, Payam, Mohraz, Minoo, Mohseni Afshar, Zeinab, Moogahi, Sasan, Mousavi Anari, Seyed Alireza, Mozaffar, Seyyed Hassan, Mozdourian, Mahnaz, Najafipour, Reza, Najari, Hamidreza, Nazemiyeh, Masoud, Norouzi, Alireza, Pourkazemi, Aydin, Poustchi, Hossein, Saberhosseini, Seyedeh Naeimeh, Saberi, Marzieh, Saber-Moghaddam, Niloufar, Sadeghi, Anahita, Sadeghi Haddad Zavareh, Mahmoud, Sahraian, Mohammad Ali, Salahi, Mehrdad, Salehi, Mohammad Reza, Sarmadian, Hossein, Sayad, Babak, Shirani, Kiana, Shirvani, Maria, Shojaei, Daryanaz, Shokri, Mehran, Siami, Zeinab, Sima, Ali Reza, Soleimani, Alireza, Soltanmohammad, Saedeh, Tabarsi, Payam, Taghizadieh, Ali, Tavassoli, Samaneh, Varnasseri, Mehran, Vaziri, Siavash, Yadyad, Mohammad Jaafar, Yaghoubi, Shoeleh, Yazdanpanah, Yalda, Yousefi, Farid, Zamanian, Mohammad Hossein, Zand, Farid, Zare Hoseinzade, Elham, Bergin, Colm, Cotter, Aoife, de Barra, Eoghan, Jackson, Arthur, Laffey, John, McCarthy, Cormac, Muldoon, Eavan, Sadlier, Corinna, Maguire, Teresa, Angheben, Andrea, Bai, Francesca, Bandera, Alessandra, Barchiesi, Francesco, Bassetti, Matteo, Bisi, Luca, Bonfanti, Paolo, Calò, Federica, Campoli, Caterina, Canovari, Benedetta, Capetti, Amedeo, Castelli, Francesco, Cauda, Roberto, Cingolani, Antonella, Cocco, Nicolò, Coppola, Nicola, Corcione, Silvia, Cremonini, Eleonora, d'Arminio Monforte, Antonella, de Gaetano Donati, Katleen, De Nardo, Pasquale, De Rosa, Francesco Giuseppe, Degioanni, Maria, Della Siega, Paola, Di Bella, Stefano, Drera, Bruno, Focà, Emanuele, Fornabaio, Chiara, Galli, Massimo, Giacomazzi, Donatella, Gori, Andrea, Gustinetti, Giulia, Iannuzzi, Francesca, Kertusha, Blerta, Lamonica, Silvia, Lichtner, Miriam, Lupia, Tommaso, Luzzati, Roberto, Macera, Margherita, Menatti, Elisabetta, Merelli, Maria, Merlini, Esther, Monari, Caterina, Pan, Angelo, Pecori, Davide, Pezzani, Diletta, Riccardi, Niccolò, Rodari, Paola, Roldan, Eugenia, Rovere, Pierangelo, Rusconi, Stefano, Scabini, Silvia, Tascini, Carlo, Viale, Pierluigi, Vincenzi, Marcello, Zuccalà, Paola, Zucchi, Patrizia, Al-Roomi, Moudhi, Al-Sabah, Salman, Schrapp, Kelly, Hassoun, Mahmoud, Matar, Madonna, Dbouni, Oussaima, Yared, Nadine, Saliba, Michele, Farra, Anna, Riachi, Moussa, Zablockiene, Birute, Reuter, Jean, Staub, Therese, Ab Wahab, Suhaila, Chew, Chun Keat, Chua, Hock Hin, Goh, Pik Pin, Lee, Heng Gee, Leong, Chee Loon, Low, Lee Lee, Mak, Wen Yao, Mohamed Gani, Yasmin, Muhamad, Dzawani, Zaidan, Nor Zaila, Ducker, Camilla, Demiri, Ilir, Aballi, Saad, Berg, Åse, Blomberg, Bjørn, Dalgard, Olav, Dyrhol-Riise, Anne Ma, Eiken, Ragnhild, Ernst, Gernot, Hannula, Ranula, Haugli, Metter, Heggelund, Lars, Hoel, Hedda, Hoff, Dag Arne Lihaug, Holten, Aleksander Rygh, Johannessen, Asgeir, Kåsine, Trine, Kildal, Anders Benjamin, Kittang, Bård Reikvam, Nezvalova-Henriksen, Katerina, Olsen, Inge Christoffer, Olsen, Roy Bjørkolt, Skei, Nina Vibeche, Skudal, Hilde, Tholin, Birgitte, Thoresen, Lars, Trøseid, Marius, Tveita, Anders, Vinge, Leif, Ystrøm, Carl Magnus, Al Jahdhami, Issa, AlNaamani, Khalid, Al Balushi, Zakariya, Pandak, Nenad, Abbas, Salma, Akhtar, Nasim, Azam, Sumeyya, Begum, Dilshad, Hassan, Sadia, Herekar, Fivzia, Khan, Shahzaib, Khan, Ejaz Ahmed, Mahmood, Syed Faisal, Nasir, Nosheen, Rahim, Anum, Sarfaraz, Samreen, Shaikh, Qurat-ul-Ain, Sultan, Faisal, Walayat, Usman, Agurto-Lescano, Erika Cecilia, Alcantara-Díaz, Andrés Martín, Alva-Correa, Ana María, Alvarado-Moreno, José Gustavo, Ángeles-Padilla, Bethsabé, Arbañil-Huamàn, Hugo César, Ávila-Reyes, Pool Christopher, Azañero-Haro, Johan Alexander, Barreto-Rocchetti, Luis Guillermo, Benitez-Peche, Jorge Marko A., Bernal-Màlaga, Karla Hortencia, Cabrera-Portillo, Liliana Norma, Carazas-Chavarry, Reynaldo Javier, Càrcamo, Paloma Mariana, Casimiro-Porras, Indira Catalina, Castillo-Espinoza, Jhuliana, Chacaltana-Huarcaya, Jesús Norberto, Cornejo-Valdivia, Carla Raquel, Cruz-Chereque, Augusto, Del-Aguila-Torres, Keith Cayetano Marcelino, Díaz-Chipana, Erika, Flores-Valdez, Neil, Franco-Vàsquez, Rosanna Andrea, Gallegos-López, Roxana Consuelo, Gastiaburú-Rodriguez, Dauma Yesenia, Gianella-Malca, Gonzalo Ernesto, Gomero-Lopez, Andrés Alonso, Hercilla-Vàsquez, Luis Enrique, Hueda-Zavaleta, Miguel Ángel, Ibarcena-Llerena, Claudia Vanessa, Iberico-Barrera, Carlos Alberto, Inquilla-Castillo, Miguel Angel, Juàrez-Eyzaguirre, Jesus Alberto, Laca-Barrera, Manuel, León-Jiménez, Franco, Luna-Wilson, Carla Vanessa, Màlaga, German, Marin, Ricardo, Mejía-Cordero, Fernando, Mendoza-Laredo, Juan Arturo, Meregildo-Rodríguez, Edinson Dante, Miranda-Manrique, Gonzalo Francisco, Olivera-Chaupis, Marco, Ortega-Monasterios, Fatima Josefina, Otazú-Ybàñez, Jimmy Pedro, Paredes-Moreno, María Angélica, Peña-Mayorga, Claudia Ximena, Peña-Vàsquez, Olivia del Carmen, Peña-Villalobos, Alejandro, Ponce, Oscar J, Ponte-Fernandez, Katherin Estefania, Pro, Jose, Quispe-Nolazco, César Miguel, Ramos-Samanez, Manuel Efrain, Rojas-Murrugarra, Kory Mirtha, Samanez-Pérez, Jorge Mauro, Sànchez-Carrillo, Halbert Chrostian, Sànchez-Garavito, Epifanio, Sànchez-Sevillano, Ricardo Manuel, Sandoval-Manrique, Hernan, Santos-Revilla, Gabriela, Silva-Ramos, Julio Antonio, Solano-Ico, Manuel Alberto, Soto, Alonso, Sotomayor-Woolcott, Giannilu Michelle, Tapia-Orihuela, Ruben Kevin Arnold, Terrazas-Obregón, Carmen Sara, Terrones-Levano, Victor Francisco, Ticona-Huaroto, Cesar Eduardo, Torres-Ninapayta, Walter, Torres-Ruiz, Oscar Martin, Ugarte-Mercado, Dario, Vargas-Anahua, Orlando José, Vàsquez-Becerra, Ruben Dario, Vàsquez-Cerro, José Gabriel, Villegas-Chiroque, Miguel, Williams, Anna Larson, Yauri-Lazo, Randi Mauricio, Abad, Cybele Lara, Andales-Bacolcol, Silverose Ann, Arcegono, Marlon, Arches, Jamie, Astudillo, Mary Grace, Aventura, Emily, Awing, Arlyn, Bala, Mishelle Vonnabie, Bello, Jia An, Blanco, Peter, Benedicto, Jubert, Buno, Susana, Cabrera, Justine, Cajulao, Thea Pamela, Caoili, Janice, Casiple-Amsua, Lina, Catambing, Victor, Chin, Inofel, Chua, Ma. Bernadette, Chua, Mitzi Marie, Climacosa, Fresthel Monica, David-Wang, Aileen, De los Reyes, Virginia, Europa, Gilly May, Fernandez, Lenora, Francisco, Jorge, Garcia, Gerard, Garcia, Jemelyn, Gler, Maria Tarcela, Isidro, Marie Grace Dawn, Javier, Rozelle Jade, Kwek, Marion, Lansang, Mary Ann, Lee, Aileen, Li, Kingbherly, Llanes, Mark Ramon Victor, Llorin, Ryan, Macadato, Omar Khayyam, Malundo, Anna Flor, Mercado, Maria Elizabeth, Mujeres, Mercedes, Nepomuceno, Marisse, Ngo-Sanchez, Katha, Orden, Mary Claire, Pablo-Villamor, Maria Philina, Paez, Ruel Dionisio, Palmes, Patricio, Panaligan, Marion, Quinivista-Yoon, Jenny Mae, Ramos, Mary Shiela Ariola, Ramos -Penalosa, Christine, Reyes, Sheila Marie, Roa, Kathryn, Roman, Arthur Dessi, Rosario, Minette Claire, Roxas, Evalyn, Santos, Lourdes Ella Gonzales, Soldevilla, Helmar, Solante, Rontgene, Suaco, Jane, Tagarda, Daisy, Tang, Issa Rufina, Te, Bob, Teo, Dennis, Tibayan, Christopher John, Villalobos, Ralph Elvi, Ymbong, Duane Richard, Zabat, Gelza Mae, Batkova, Stepanka, Cardoso, Orlando, Garrote, Ana-Raquel, Lino, Sara, Manata, Maria-José, Pinheiro, Helder, Póvoas, Diana, Ramirez, Freddy, Seixas, Diana, Naji, Assem, Al Gethamy, M Al, AL-Mulaify, Mohammed Sami, Al Maghraby, Reem, Alrajhi, A, Al Sharidi, Aynaa, Alotaibi, Naif, AlShaharani, F, Barry, Mazin, Ghonem, Leen, Khalel, Anas, Kharaba, Ayman Mohammed, Alabdan, Lulwah, AlAbdullah, Mohammed Sharaf, Al Shabib, Abdullah, Bengu, Simangele, Bennet, Jacklyn, Dubula, Thozama, Howell, Pauline, Janse van Vuuren, Cloete, Kalla, Ismail, Lifson, Aimee, Maasdorp, Shaun, Magua, Nombulelo, Maluleke, Vongani, Mbhele, Nokuphiwa, Mdladla, Nathi, Mendelson, Mark, Menezes, Colin, Mwelase, Thando, Nchabeleng, Maphoshane, Palanee-Phillips, Thesla, Parker, Victoria, Rassool, Mohammed, Reeder, Paul, Sossen, Bianca, Steyn, Dewald, Tsitsi, Merika, van Blydenstein, Sarah Alex, Venter, Michelle, Van Vuuren, Janse, Venturas, Jacquie, Abad Pérez, Daniel, Abenza, Maria José, Alarcón-Payer, Carolina, Armero Garrigos, Eva, Arribas, Jose Ramon, Ascaso, Ana, Berenguer, Juan, Cabello-Clotet, Noemí, Chamorro Tojeiro, Sandra, Cuenca-Acevedo, Rafael, de la Calle, Fernando, Del Toro, Maria Dolores, Díaz Pollàn, Beatriz, Diez, Cristina, Esquillor-Rodrigo, María José, Estrada, Vicente, Fanciulli, Chiara, Fanjul, Francisco, Fernàndez de Orueta, Lucía, Ferre, Adrian, Ferreira Pasos, Eva Maria, Gainzarain-Arana, Juan-Carlos, Garcia, Felipe, García Deltoro, Miguel, Goikoetxea Agirre, Ane Josune, Gómez Barquero, Julia, Gomez-Huelgas, Ricardo, Gonzàlez Moraleja, Julio, Guijarro, Carlos, Gutierrez, Felix, Guzmàn, Jesús, Ibarguren, Maialen, Iribarren, Jose Antonio, Jerusalem, Koen, Juan Arribas, Arturo, Lalueza, A, Leone, Antonio, Lopez Azkarreta, Iñigo, Lozano-Martin, Daniel, Lucendo, Alfredo J, Luengo López, Mariella, Martín Oterino, JA, Masa, JF, Merino, Esperanza, Monge-Maillo, Begoña, Moran-Rodríguez, Miguel-Angel, Muñez Rubio, Elena, Muñoz Sanchez, Josefa, Nuñez Orantos, Maria Jose, Nuño, Enrique, Ortiz-De-Zarate-Ibarra, Zuriñe, Pagàn-Muñoz, Bàrbara, Paño-Pardo, José Ramón, Peñaranda, Maria, Pérez Chica, Gerardo, Pérez Fernàndez, AM, Pérez-López, Carmen, Polo San Ricardo, Victor, Portu-Zapirain, Joseba, Puchades, Francesco, Rivas Paterna, Ana Belen, Rodríguez Vidigal, Francisco F, Rodríguez-Baño, Jesus, Ruiz-Seco, Pilar, Ryan, Pablo, Saez-De-Adana, Ester, Salas, Rosario, Salavert Lletí, Miguel, Sandoval, Raquel, Toyas-Miazza, Carla, Valencia, Jorge, Vargas, Emilio, Velasco, Maria, Von Wichmann, Miguel Angel, Bosshard, Andreas, Calmy, Alexandra, Castro, Tiago, Cavassini, Matthias, Clerc, Olivier, Conen, Anna, Desbaillets, Nicolas, Desgranges, Florian, Duss, Francois, Emonet, Stephane, Erard, Veronique, Eyer, Myriam, Fayet-Mello, Aurélie, Flammer, Yvonne, Friedl, Andrée, Fulchini, Rosamaria, Furrer, Hansjakob, Garin, Nicolas, Gastberger, Salome, Greiner, Michael, Haefliger, David, Haubitz, Sebastian, Hoffmann, Matthias, Isenring, Egon, Jakopp, Barbara, Lampert, Markus, Marinosci, Annalisa, Martin, Yvonne, Petignat, Pierre-Auguste, Piso, Rein Jan, Prendki, Virginie, Rutishauser, Jonas, Schaefer, Elisabeth, Schmiedel, Yvonne, Schwery, Stefan, Stavropoulou, Elisavet, Stoeckle, Marcel, Suttels, Veronique, Thurnher, Maria Christine, van den Bogaart, Lorena, West, Emily, Wiegand, Jan, and Wiggli, Benedikt

Published
2022
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4. Electrodiagnostic evaluation in diabetes mellitus: A study based on case series in 72 diabetic patients.

Author

Dube, Suchitra, Hulke, Sandip M., Thakare, Avinash E., Khadanga, Sagar, Wakode, Santosh L., Bharshankar, Rajay N., and Pakhare, Abhijit

Subjects
TYPE 2 diabetes, NERVE conduction studies, TIBIAL nerve, PERONEAL nerve, MOTOR neuron diseases
Abstract

ABSTRACT: Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes; so, a nerve conduction study (NCS) is conducted to detect the type of neuropathy that is present. To discuss the NCS findings in diabetic patients. An observational study was conducted in the Physiology Department of AIIMS, Bhopal, in collaboration with the Medicine Department of the Institute. Seventy-two diagnosed type 2 diabetes mellitus (T2DM) patients were examined using NCS (Nihon Kohden Neuropack XI Machine). Microsoft Excel was utilized for data compilation and result analysis. Based on NCS, 94% of patients were abnormal, and 6% were normal. Of abnormal patients, 89% had asymmetrical involvement, and 5% had symmetrical involvement. About 74% had mixed neuropathy, 11% had motor neuropathy, and 10% had sensory neuropathy. Mixed involvement was seen in 60% of patients and axonal involvement in 35% of patients, and 5% were normal. Lower limb involvement was seen predominately. The most common bilaterally involved motor nerve was the peroneal nerve, seen in 49% of cases, whereas the most common bilaterally involved sensory nerve was the sural nerve involved in 59% of cases. The left tibial nerve was the most common unilaterally involved motor nerve seen in 32% of cases, and the left sural nerve was the most common sensory nerve involved in 54% of cases. Asymmetric sensorimotor involvement with mixed involvement (axonal + demyelinating) was seen in diabetic patients. Peroneal and sural nerves were the most common bilaterally involved motor and sensory nerves, respectively. Similarly, the left tibial and left sural nerves were the most common unilaterally affected motor and sensory nerves, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Experience of rigorous practice of antimicrobial stewardship program and its impact on antibiotic consumption.

Author

Kumar, Shweta, Khadanga, Sagar, Haq, Rehan Ul, Saxena, Pradeep, Sampath, Ananyan, and Tadepalli, Karuna

Subjects
*ANTIMICROBIAL stewardship, *INTENSIVE care units, *DRUG resistance in microorganisms, *MEDICAL research, *COLISTIN
Abstract

Objectives: Antimicrobial resistance (AMR) is the next pandemic with a huge global economic burden. A customized antimicrobial stewardship program (AMSP) is a well-recognized tool for containment of AMR. The current study was undertaken to identify the impact of AMSP measures on the trends of antibiotic consumption rates, e.g., defined daily dose (DDD) and days of therapy (DOT) per 100 patient days in pre-identified areas of the hospital. Materials and Methods: The present study was a case-record-based extended cross-sectional study carried out in a tertiary care institute in central India from January 2021 to September 2021. Time-bound feasible sampling was undertaken, and all cases were included without any exclusion criteria. The study was approved by the Ethics Committee of the Institute and funded by the Indian Council of Medical Research (ICMR). Statistical Analysis: The data was entered into a spreadsheet. The calculation of DDD and DOT was performed individually for each antibiotic as per WHO tool per 100 patient days. Results: In high-priority areas such as intensive care unit and high dependency unit, the top three DDD/100 days were meropenem (77.9), colistin (41.4), and piperacillin-tazobactam (13.5) versus the top 3 DOT/100 patient days were meropenem (40.5), colistin (20.9), and piperacillin-tazobactam (15.7). In low-priority areas such as general wards, the top 3 DDD/100 patient days were meropenem (45.4), piperacillin-tazobactam (22.6), and cefoperazone (5.0) versus the top three DOT/100 patient days were meropenem (45.4), cefoperazone (44.9), and piperacillintazobactam (22.6). There was a downward trend of consumption of almost all these antibiotics when measured at baseline and the next two quarters. DDD and DOT per 100 patient days had a positive linear correlation during the study period. Conclusions: The present study demonstrates about 50% reduction in consumption of ICMR priority antibiotics with a yearlong AMSP. DDD and DOT per 100 patient days can be calculated with minimal effort, even in extremely busy hospitals. DOT tools are less labor-intensive and, hence, should be the pivotal tool for antibiotic exposure measurement. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Diagnostic Utility of the "Lancet Consensus Scoring System" in Suspected Cases of Tuberculous Meningitis in Patients Attending a Tertiary Care Hospital in Central India.

Author

Patel, Sakshi, Dadheech, Malti, Sahu, Pushpendra, Pathe, Chandrashekhar, Singh, Jitendra, Purwar, Shashank, Rai, Nirendra Kumar, Khadanga, Sagar, Gupta, Radha Sarawagi, and Maurya, Anand Kumar

Abstract

Background: Tuberculous meningitis (TBM) is a challenging condition to diagnose, and current laboratory methods have limitations, especially in developing countries. This study evaluated the diagnostic utility of the Lancet consensus scoring (LCS) system in suspected TBM cases at a tertiary care center in India. Methods: The clinical manifestations of 75 patients with suspected TBM were prospectively evaluated using the LCS system, which categorizes cases as "definite" (laboratory confirmed), "probable" (>10 points without imaging or > 12 points with imaging), and "possible" (6–9 points without imaging or 6–11 points with imaging). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the LCS system were compared to GeneXpert and mycobacterial growth indicator tube (MGIT) liquid culture, which are considered the gold standard tests. Results: Out of 75 recruited cases, 11 were confirmed as definite TBM cases. The LCS system correctly identified 8 of these as "probable" and 3 as "possible" cases. Compared to GeneXpert, the LCS system had a specificity of 100%, sensitivity of 54.55%, NPV of 92.7%, and PPV of 100%. Compared to MGIT liquid culture, the LCS system had a specificity of 100%, sensitivity of 81.82%, NPV of 96.97%, and PPV of 100%. The diagnostic accuracy was higher for MGIT liquid culture (97.33%) than GeneXpert (93.33%). Conclusion: The LCS system is a practical and easily applicable tool that can aid the diagnosis of suspected TBM cases, particularly in resource-limited settings. Despite some limitations, the LCS system demonstrated good diagnostic performance, suggesting its potential utility in improving TBM diagnosis and management. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Predictors of thirty-day mortality among patients with blood stream infection with WHO priority pathogens: single centre exploratory study from a referral teaching hospital in central India.

Author

Budhiraja, Akshit, Karuna, Tadepalli, Khan, Farhan, Kumar, Shweta, Shaji, Namitha, Bajaj, Ehsaas, Purwar, Shashank, Pakhare, Abhijit, Joshi, Rajnish, Saigal, Saurabh, and Khadanga, Sagar

Subjects
MULTIDRUG resistance in bacteria, TEACHING hospitals, MORTALITY, RESPIRATORY infections, PATHOGENIC microorganisms
Abstract

Background and Objectives: Bloodstream infection (BSI) is defined by the presence of viable microorganisms in the bloodstream. BSI is one of the major causes of sepsis and subsequent adverse clinical outcomes all across the globe. The present study was undertaken to identify clinico-epidemio-microbiological variables associated with 30-day mortality in patients having BSI with WHO priority pathogens. Materials and Methods: The study was conducted at a public sector tertiary care institute in central India from April 2019 to March 2021. Blood samples collected from patients with clinical suspicion of sepsis, were processed by automated bacterial culture system and interpreted as per CLSI guidelines. Calculated sample size was 150. Data was analyzed by R software. Results: Respiratory tract infection was the most common source (43.3%) of BSI, followed by the gastrointestinal (20%) and urinary tract (18.7%). Among the patients, 33% required invasive mechanical ventilation, and 31% required inotropes. Diabetes mellitus (DM) was the most common co-morbidity (34%). The incidence of multi-drug resistant organisms (MDRO) was 59.3%. Escherichia coli was the most commonly (24%) isolated organism, followed by Klebsiella pneumoniae (17.3%) and Acinetobacter baumannii (16%). Conclusion: Higher age, higher qSOFA score / SIRS score / mean SOFA score at presentation had higher mortality. Use of mechanical ventilation and inotropes during treatment and isolation of critical category organisms of WPP and multi drug resistant organisms were independent 30-day mortality predictors. [ABSTRACT FROM AUTHOR]

Published
2024

13. A multicentre, double-blind, placebo-controlled randomized trial of Mycobacterium w in critically ill patients with COVID-19 (ARMY-2).

Author

Sehgal, Inderpaul S., Agarwal, Ritesh, Jindal, Atul, Siddiqui, Md Sabah, Mohan, Anant, Pal, Arnab, Guleria, Randeep, Bhalla, Ashish, Kajal, Kamal, Malhotra, Pankaj, Puri, Goverdhan Dutt, Khadanga, Sagar, Joshi, Rajnish, Singh, Sarman, Saigal, Saurabh, Nagarkar, Nitin M., Suri, Vikas, Bhatnagar, Sushma, Tiwari, Pawan, and Singh, Mini P.

Subjects
COVID-19, MYCOBACTERIUM, CRITICALLY ill, SURVIVAL rate, ODDS ratio
Abstract

Background: Mycobacterium w (Mw), an immunomodulator, resulted in better clinical status in severe coronavirus infectious disease 19 (COVID-19) but no survival benefit in a previous study. Herein, we investigate whether Mw could improve clinical outcomes and survival in COVID-19. Materials and Methods: In a multicentric, randomized, double-blind, parallel-group, placebo-controlled trial, we randomized hospitalized subjects with severe COVID-19 to receive either 0.3 mL/day of Mw intradermally or a matching placebo for three consecutive days. The primary outcome was 28-day mortality. The co-primary outcome was the distribution of clinical status assessed on a seven-point ordinal scale ranging from discharged (category 1) to death (category 7) on study days 14, 21, and 28. The key secondary outcomes were the change in sequential organ failure assessment (SOFA) score on days 7 and 14 compared to the baseline, treatment-emergent adverse events, and others. Results: We included 273 subjects (136 Mw, 137 placebo). The use of Mw did not improve 28-day survival (Mw vs. placebo, 18 [13.2%] vs. 12 [8.8%], P = 0.259) or the clinical status on days 14 (odds ratio [OR], 1.33; 95% confidence intervals [CI], 0.79-2.3), 21 (OR, 1.49; 95% CI, 0.83-2.7) or 28 (OR, 1.49; 95% CI, 0.79-2.8) between the two study arms. There was no difference in the delta SOFA score or other secondary outcomes between the two groups. We observed higher injection site reactions with Mw. Conclusion: Mw did not reduce 28-day mortality or improve clinical status on days 14, 21 and 28 compared to placebo in patients with severe COVID-19. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Melioidosis as a Mystique Infection: A Study From Central India

Author

Yadav, Vaibhav, primary, Pawar, Akash, additional, Meena, Mahadev, additional, Khadanga, Sagar, additional, Gupta, Ayush, additional, Dandasena, Tarini Prasad, additional, Singhai, Abhishek, additional, Joshi, Rajnish, additional, Saigal, Saurabh, additional, and Atlani, Mahendra, additional

Published
2023
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19. Changing Trends in Antimicrobial Susceptibility Patterns of Bloodstream Infection (BSI) in Secondary Care Hospitals of India

Author

Karuna, T, primary, Gupta, Ayush, additional, Vyas, Apurva, additional, Kumar, Shweta, additional, Sampath, Ananyan, additional, Goel, Pramod, additional, Shukla, Pankaj, additional, Mishra, Vivek, additional, Sharma, Sandeep, additional, Chakraborty, Sourabh, additional, Jaiswal, Shree Prakash, additional, Mishra, Abhi, additional, Gupta, Apoorwa, additional, Sahu, Manisa, additional, Tiwari, Shreshtha, additional, Pal, Anisa, additional, Nagendra, Manish, additional, Gautham, Harish, additional, Patel, Kamlesh, additional, Asati, Shruti, additional, and Khadanga, Sagar, additional

Published
2023
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24. Point Prevalence Study (PPS) of Antibiotic Usage and Bacterial Culture Rate (BCR) among Secondary Care Hospitals of Small Cities in Central India: Consolidating Indian Evidence

Author

Kumar, Shweta, additional, Shukla, Pankaj, additional, Goel, Pramod, additional, Mishra, Vivek, additional, Gupta, Ayush, additional, Karuna, Tadepalli, additional, Srivastava, Rakesh, additional, Gupta, Amit, additional, Baharani, Deepak, additional, Pansey, Parijat, additional, Chandiwal, Sunil, additional, Shrivastava, Sandeep, additional, Gupta, Ankur, additional, Rajpoot, Shailendra Singh, additional, Biswal, DebaDulal, additional, Ansari, Mehrunnisa, additional, Walia, Kamini, additional, and Khadanga, Sagar, additional

Published
2023
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27. Detection of specific antibodies against SARS-CoV-2 spike protein via ultra-sensitive bio-functionalized carbonnitride-reduced graphene oxide electrochemical immunosensing platform in real samples

Author

Sadique, Mohd. Abubakar, primary, Yadav, Shalu, additional, Ranjan, Pushpesh, additional, Chouhan, Raghuraj Singh, additional, Jerman, Ivan, additional, Kumar, Ashok, additional, Saigal, Saurabh, additional, Khadanga, Sagar, additional, Khan, Raju, additional, and Srivastava, Avanish K., additional

Published
2023
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31. Health-care-associated bloodstream and urinary tract infections in a network of hospitals in India: a multicentre, hospital-based, prospective surveillance study

Author

Mathur, Purva, primary, Malpiedi, Paul, additional, Walia, Kamini, additional, Srikantiah, Padmini, additional, Gupta, Sunil, additional, Lohiya, Ayush, additional, Chakrabarti, Arunaloke, additional, Ray, Pallab, additional, Biswal, Manisha, additional, Taneja, Neelam, additional, Rupali, Priscilla, additional, Balaji, Veeraraghavan, additional, Rodrigues, Camilla, additional, Lakshmi Nag, Vijaya, additional, Tak, Vibhor, additional, Venkatesh, Vimala, additional, Mukhopadhyay, Chiranjay, additional, Deotale, Vijayshri, additional, Padmaja, Kanne, additional, Wattal, Chand, additional, Bhattacharya, Sanjay, additional, Karuna, Tadepalli, additional, Behera, Bijayini, additional, Singh, Sanjeev, additional, Nath, Reema, additional, Ray, Raja, additional, Baveja, Sujata, additional, Fomda, Bashir A, additional, Sulochana Devi, Khumanthem, additional, Das, Padma, additional, Khandelwal, Neeta, additional, Verma, Prachi, additional, Bhattacharyya, Prithwis, additional, Gaind, Rajni, additional, Kapoor, Lata, additional, Gupta, Neil, additional, Sharma, Aditya, additional, VanderEnde, Daniel, additional, Siromany, Valan, additional, Laserson, Kayla, additional, Guleria, Randeep, additional, Malhotra, Rajesh, additional, Katoch, Omika, additional, Katyal, Sonal, additional, Khurana, Surbhi, additional, Kumar, Subodh, additional, Agrawal, Richa, additional, Dev Soni, Kapil, additional, Sagar, Sushma, additional, Wig, Naveet, additional, Garg, Pramod, additional, Kapil, Arti, additional, Lodha, Rakesh, additional, Sahu, Manoj, additional, Misra, M.C., additional, Lamba, Mamta, additional, Jain, Shristi, additional, Paul, Hema, additional, Sarojini Michael, Joy, additional, Kumar Bhatia, Pradeep, additional, Singh, Kuldeep, additional, Gupta, Neeraj, additional, Khera, Daisy, additional, Himanshu, D, additional, Verma, Sheetal, additional, Gupta, Prashant, additional, Kumar, Mala, additional, Pervez Khan, Mohammed, additional, Gupta, Sarika, additional, Kalwaje Eshwara, Vandana, additional, Varma, Muralidhar, additional, Attal, Ruchita, additional, Sudhaharan, Sukanya, additional, Goel, Neeraj, additional, Saigal, Saurabh, additional, Khadanga, Sagar, additional, Gupta, Ayush, additional, Thirunarayan, M.A., additional, Sethuraman, Nandini, additional, Roy, Ujjaini, additional, Jyoti Raj, Hirak, additional, D'Souza, Desma, additional, Chandy, Mammen, additional, Mukherjee, Sudipta, additional, Kumar Roy, Manas, additional, Goel, Gaurav, additional, Tripathy, Swagata, additional, Misra, Satyajeet, additional, Dey, Anupam, additional, Misra, Tushar, additional, Ranjan Das, Rashmi, additional, Bashir, Gulnaz, additional, Nazir, Shaista, additional, Ranjana Devi, Khuraijam, additional, Chaoba Singh, Langpoklakpam, additional, Bhargava, Anudita, additional, Gaikwad, Ujjwala, additional, Vaghela, Geeta, additional, Sukharamwala, Tanvi, additional, Ch. Phukan, Anil, additional, Lyngdoh, Clarissa, additional, Saksena, Rushika, additional, Sharma, Rajeev, additional, and Velayudhan, Anoop, additional

Published
2022
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34. Changing Critical Care Patterns and Associated Outcomes in Mechanically Ventilated Severe COVID-19 Patients in Different Time Periods: An Explanatory Study from Central India

Author

Joshi, Rajnish, primary, Singh, Pooja, additional, Goyal, Abhishek, additional, Saigal, Saurabh, additional, Pakhare, Abhijit, additional, Sharma, Jai Prakash, additional, Anand, Abhijeet, additional, Panda, Rajesh, additional, Kodamanchili, Saiteja, additional, Karna, Sunaina Tejpal, additional, Khurana, Alkesh, additional, Brahmam, Dodda, additional, Jha, Surya, additional, Joshi, Ankur, additional, Niwariya, Yogesh, additional, and Khadanga, Sagar, additional

Published
2022
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36. Clinico–Epidemio-Microbiological Exploratory Review Among COVID-19 Patients with Secondary Infection in Central India

Author

Karuna,T, Garg,Rahul, Kumar,Shweta, Singh,Gyanendra, Prasad,Lakshmi, Krishen Pandita,Kawal, Pakhare,Abhijit, Saigal,Saurabh, Khurana,Alkesh Kumar, Joshi,Rajnish, Walia,Kamini, Khadanga,Sagar, Karuna,T, Garg,Rahul, Kumar,Shweta, Singh,Gyanendra, Prasad,Lakshmi, Krishen Pandita,Kawal, Pakhare,Abhijit, Saigal,Saurabh, Khurana,Alkesh Kumar, Joshi,Rajnish, Walia,Kamini, and Khadanga,Sagar

Abstract

T Karuna,1 Rahul Garg,2 Shweta Kumar,2 Gyanendra Singh,3 Lakshmi Prasad,4 Kawal Krishen Pandita,4 Abhijit Pakhare,3 Saurabh Saigal,5 Alkesh Kumar Khurana,6 Rajnish Joshi,2 Kamini Walia,7 Sagar Khadanga2 1Department of Microbiology, AIIMS, Bhopal, Madhya Pradesh, India; 2Department of General Medicine, AIIMS, Bhopal, Madhya Pradesh, India; 3Department of Community and Family Medicine, AIIMS, Bhopal, Madhya Pradesh, India; 4Department of Hospital Administration, AIIMS, Bhopal, Madhya Pradesh, India; 5Department of Critical Care, AIIMS, Bhopal, Madhya Pradesh, India; 6Department of Pulmonary Medicine & TB, AIIMS, Bhopal, Madhya Pradesh, India; 7Indian Council of Medical Research, New Delhi, IndiaCorrespondence: Sagar Khadanga, Department of General Medicine, AIIMS, Bhopal, Madhya Pradesh, India, Email drsagarkhadanga@gmail.comPurpose: Secondary infections (SI) in COVID-19 have been documented from 3.6% to 72% in various studies with mortality ranging from 8.1% to 57.6%. There is a gap in knowledge for clinico–epidemio-microbilogical association among COVID-19 patients with concomitant SI.Patients and Methods: This is a retrospective chart review, in central India. The study was undertaken for hospitalized adult patients during 1st June 2020 to 30th November 2020, with laboratory proven COVID-19 infection and secondary infection.Results: Out of the total 2338 number of patients, only 265 (11.3%) patients were investigated for microbiological identification of SI. Male gender was predominant (76.8%) and the mean age was 53.7 ± 17.8 years. Only 3.5% (82/2338) of patients were having microbiologically confirmed (bacterial or fungal) SI. The overall mortality was 50.9% (54/82) with a differential mortality of 88.8% (48/54) in high-priority areas and 21.4% (6/28) in low-priority areas. Blood was the most commonly investigated sample (56%) followed by urine (20.7%) and respiratory secretion (15.8%). A. baumanii complex (20/82, 24.3%) was the most common bacteria isolat

Published
2022

39. Clinico–Epidemio-Microbiological Exploratory Review Among COVID-19 Patients with Secondary Infection in Central India

Author

Karuna, T, primary, Garg, Rahul, additional, Kumar, Shweta, additional, Singh, Gyanendra, additional, Prasad, Lakshmi, additional, Krishen Pandita, Kawal, additional, Pakhare, Abhijit, additional, Saigal, Saurabh, additional, Khurana, Alkesh Kumar, additional, Joshi, Rajnish, additional, Walia, Kamini, additional, and Khadanga, Sagar, additional

Published
2022
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40. Evaluation of interleukin-33 & sST2 levels in type-2 diabetic mellitus patients with or without metabolic syndrome.

Author

Singh, Himadri, Khadanga, Sagar, Goel, Sudhir K., Majumder, Syamantak, Baig, Mirza S., Bhatia, Vikas, Chaudhary, Neha, and Saluja, Rohit

Subjects
*INTERLEUKIN-33, *METABOLIC syndrome, *PEOPLE with diabetes, *BLOOD sugar, *INSULIN sensitivity, *HYPERGLYCEMIA
Abstract

Background & objectives: Diabetes mellitus (DM) is characterized by increase in blood glucose levels due to defective insulin secretion or insulin sensitivity. Interleukins (ILs) are known to play an important role in the pathogenesis of DM. The aim of this study was to investigate the serum concentration of IL-33 and its receptor soluble ST2 (sST2) in patients with diabetes and draw a correlation between their serum levels and different standard glycaemic indices of patients affected with type-2 diabetes with or without metabolic syndrome. Methods: Thirty type-2 diabetic individuals and 30 healthy controls were recruited for this study. Serum and plasma were separated by centrifugation of blood for quantitative measurement of IL-33, sST2 and other biochemical parameters. Results: It was observed that serum IL-33 levels were significantly less and sST2 levels were significantly high in type-2 diabetic individuals as compared to healthy controls. A significant correlation between the serum IL-33 concentration and fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) levels were also found. Additionally, data also elucidated that serum levels of high-density lipoprotein, low-density lipoprotein or triglyceride in type-2 diabetics did not influence the serum levels of IL-33 and sST2, thereby excluding these factors as the major drivers of changes in serum IL-33 and sST2 concentration. Interpretation & conclusions: This study demonstrated alteration in serum levels of IL-33 and sST2 in type-2 diabetic individuals. Further mechanistic studies, focusing on the progression of type-2 diabetes could elucidate the involvement of IL-33 in the cellular acquisition of insulin resistance as observed in type-2 diabetics. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Pharmacogenomics and Personalized Medicine in Type 2 Diabetes Mellitus: Potential Implications for Clinical Practice

Author

Venkatachalapathy,Poongothai, Padhilahouse,Sruthi, Sellappan,Mohan, Subramanian,Tharunika, Kurian,Shilia Jacob, Miraj,Sonal Sekhar, Rao,Mahadev, Raut,Ashwin Ashok, Kanwar,Rupinder Kaur, Singh,Jitendra, Khadanga,Sagar, Mondithoka,Sukumar, and Munisamy,Murali

Subjects
Pharmacogenomics and Personalized Medicine
Abstract

Poongothai Venkatachalapathy,1 Sruthi Padhilahouse,1 Mohan Sellappan,1 Tharunika Subramanian,2 Shilia Jacob Kurian,3 Sonal Sekhar Miraj,3 Mahadev Rao,3 Ashwin Ashok Raut,4 Rupinder Kaur Kanwar,4 Jitendra Singh,4 Sagar Khadanga,5 Sukumar Mondithoka,5 Murali Munisamy4 1Department of Pharmacy Practice, Karpagam College of Pharmacy, Coimbatore, Tamilnadu, India; 2Karpagam Faculty of Medical Sciences and Research, Coimbatore, India; 3Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India; 4Translational Medicine Centre, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India; 5Department of General Medicine, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, IndiaCorrespondence: Murali MunisamyTranslational Medicine Centre, All India Institute of Medical Sciences, Bhopal, 462020, Madhya Pradesh, India, Tel +91-9600-883-284Email murali.tmc@aiimsbhopal.edu.inAbstract: Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, and is rising in incidence with widespread prevalence. Multiple gene variants are associated with glucose homeostasis, complex T2DM pathogenesis, and its complications. Exploring more effective therapeutic strategies for patients with diabetes is crucial. Pharmacogenomics has made precision medicine possible by allowing for individualized drug therapy based on a patient’s genetic and genomic information. T2DM is treated with various classes of oral hypoglycemic agents, such as biguanides, sulfonylureas, thiazolidinediones, meglitinides, DPP4 inhibitors, SGLT2 inhibitors, α-glucosidase inhibitors, and GLP1 analogues, which exhibit various pharmacogenetic variants. Although genomic interventions in monogenic diabetes have been implemented in clinical practice, they are still in the early stages for complex polygenic disorders, such as T2DM. Precision DM medicine has the potential to be effective in personalized therapy for those suffering from various forms of DM, such as T2DM. With recent developments in genetic techniques, the application of candidate-gene studies, large-scale genotyping investigations, genome-wide association studies, and “multiomics” studies has begun to produce results that may lead to changes in clinical practice. Enhanced knowledge of the genetic architecture of T2DM presents a bigger translational potential. This review summarizes the genetics and pathophysiology of T2DM, candidate-gene approaches, genome-wide association studies, personalized medicine, clinical relevance of pharmacogenetic variants associated with oral hypoglycemic agents, and paths toward personalized diabetology.Keywords: pharmacogenomics, personalized medicine, type 2 diabetes, antidiabetic drugs

Published
2021

48. Diagnostic Accuracy of Point of Care Tests Measuring Glycosylated Haemoglobin (HbA1c) for Glycemic Control: A Field Study in India

Author

Khadanga, Sagar, Pakhare, Abhijit, and Joshi, Rajnish

Subjects
allergology
Abstract

Objectives: This study was performed to estimate diagnostic accuracy of the two commercially available point-of-care tests to identify poor glycemic control defined by HbA1c levels, with HPLC as a reference. Settings: The study was carried at two locations, general medical out-patient department of a teaching medical college in Bhopal (urban), and a primary health care centre in rural area in the state of Madhya Pradesh, India.Participants: All individuals with diabetes mellitus who presented to the health care facility for assessment of glycemic control. We excluded participants who denied a written informed consent. No other exclusions were used. We compared HbA1c estimated from two index tests (Hemocue Hb501, Sweden; SD Biosensor, South Korea) from capillary blood samples with HPLC performed from venous blood, as a reference standard. Primary and secondary outcome measures: Diagnostic properties of index tests such as sensitivity, specificity, positive and negative predictive value and diagnostic accuracy for identifying poor glycemic control were primary outcome measures. Lin’s concordance correlation coefficient (CCC) was secondary outcome measure.Results: Out of 114 patients, all received reference standard, 103-Hemocue A1C test, and 110- SD Biosensor test. Overall both the index-tests had similar diagnostic accuracy estimates. The area under the Receiver Operating Curve for SDA1c device was 0.935 (95%CI 0.886-0.983), and for Hemocue device was 0.938 (95%CI 0.893-0.984). The Hemocue device HbA1c value of above 7.0 (positive) correctly predicted poor glycemic control 92% times (81.58% for SD device). There were 4 vs. 11 device failures and 14 vs. 12 failures with SD and Hemocue respectively. Ambient air temperatures were no different for the device test failures.Conclusions: Commercially available point-of-care tests evaluated in this study are comparable and an acceptable alternative to HPLC based measurements for assessment of glycemic control. Tests and device failure rates of both the index tests are similar.

Published
2021

49. Status of hypertension control in urban slums of Central India : A Community health worker based two-year follow up

Author

Pakhare Abhijit, Lahiri Anuja, Shrivastava Neelesh, N Subba K, Veera Durga Vaishnavi Kurra, Joshi Ankur, Atal Shubham, Khadanga Sagar, and Joshi Rajnish

Subjects
education.field_of_study, Multivariate analysis, business.industry, Population, Anthropometry, medicine.disease, Diabetes mellitus, Cohort, Community health, medicine, Social determinants of health, business, education, Stroke, Demography
Abstract

BackgroundHypertension is a leading cause of cardiovascular diseases and its control is poor. There is heterogeneity in levels of blood-pressure control among various population sub-groups. The present study was conducted within the framework of National Program for prevention and control of cancer, diabetes, cardiovascular diseases and stroke (NPCDCS) in India. It aims to estimate proportion of optimal blood pressure control and identify potential risk factors pertaining to uncontrolled hypertension consequent to initial screening.MethodsWe assembled a cohort of individuals with hypertension confirmed in a baseline screening in sixteen urban slum clusters of Bhopal (2017-2018). Sixteen Accredited Social Health Activists (ASHAs) were trained from within these slums. Individuals with hypertension were linked to primary care providers and followed-up for next two years. Obtaining optimal blood-pressure control (defined as SBP< 140 and DBPResultsOf a total of 6174 individuals, 1571 (25.4%) had hypertension, of which 813 were previously known and 758 were newly detected during baseline survey. Two year follow up was completed for 1177 (74.9%). Blood-pressure was optimally controlled in 301 (26%) at baseline, and in 442 (38%) individuals at two years (absolute increase of 12%; 95% CI 10.2-13.9). Older age, physical-inactivity, higher BMI and newly diagnosed hypertension were significantly associated with uncontrolled blood-pressure.ConclusionsWe found about six of every ten individuals with hypertension were on-treatment, and about four were optimally controlled. These findings provide a benchmark for NPCDCS, in terms of achievable goals within short periods of follow-up.

Published
2021
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