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3. Chinese Public’s Engagement in Preventive and Intervening Health Behaviors During the Early Breakout of COVID-19: Cross-Sectional Study

Author

Niu, Zhaomeng, Wang, Tingting, Hu, Pengwei, Mei, Jing, and Tang, Zhihan

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundSince January 2020, the coronavirus disease (COVID-19) swept over China and then the world, causing a global public health crisis. People’s adoption of preventive and intervening behaviors is critical in curbing the spread of the virus. ObjectiveThe aim of this study is to evaluate Chinese people’s adoption of health behaviors in responding to COVID-19 and to identify key determinants for their engagement. MethodsAn anonymous online questionnaire was distributed in early February 2020 among Mainland Chinese (18 years or older) to examine their engagement in preventive behaviors (eg, frequent handwashing, wearing masks, staying at home) and intervening behaviors (eg, advising family to wash hands frequently), and to explore potential determinants for their adoption of these health behaviors. ResultsOut of 2949 participants, 55.3% (n=1629) reported frequent engagement in preventive health behaviors, and over 84% (n=2493) performed at least one intervening health behavior. Greater engagement in preventive behaviors was found among participants who received higher education, were married, reported fewer barriers and greater benefits of engagement, reported greater self-efficacy and emotional support, had greater patient-centered communication before, had a greater media literacy level, and had greater new media and traditional media use for COVID-19 news. Greater engagement in intervening behaviors was observed among participants who were married, had lower income, reported greater benefits of health behaviors, had greater patient-centered communication before, had a lower media literacy level, and had a greater new media and traditional media use for COVID-19 news. ConclusionsParticipants’ engagement in coronavirus-related preventive and intervening behaviors was overall high, and the associations varied across demographic and psychosocial variables. Hence, customized health interventions that address the determinants for health behaviors are needed to improve people’s adherence to coronavirus-related behavior guidelines.

Published
2020
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4. A Cross-Cultural Comparison of an Extended Planned Risk Information Seeking Model on Mental Health Among College Students: Cross-Sectional Study

Author

Niu, Zhaomeng, Willoughby, Jessica Fitts, Mei, Jing, Li, Shaochun, and Hu, Pengwei

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundApproximately 42.5 million adults have been affected by mental illness in the United States in 2013, and 173 million people have been affected by a diagnosable psychiatric disorder in China. An increasing number of people tend to seek health information on the Web, and it is important to understand the factors associated with individuals’ mental health information seeking. Identifying factors associated with mental health information seeking may influence the disease progression of potential patients. The planned risk information seeking model (PRISM) was developed in 2010 by integrating multiple information seeking models including the theory of planned behavior. Few studies have replicated PRISM outside the United States and no previous study has examined mental health as a personal risk in different cultures. ObjectiveThis study aimed to test the planned risk information seeking model (PRISM) in China and the United States with a chronic disease, mental illness, and two additional factors, ie, media use and cultural identity, among college students. MethodsData were collected in both countries using the same online survey through a survey management program (Qualtrics). In China, college instructors distributed the survey link among university students, and it was also posted on a leading social media site called Sina Weibo. In the United States, the data were collected in a college-wide survey pool in a large Northwestern university. ResultsThe final sample size was 235 for the Chinese sample and 241 for the US sample. Media use was significantly associated with mental health information–seeking intentions in the Chinese sample (P

Published
2020
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6. Medical visual question answering using joint self-supervised learning

Author

Zhou, Yuan, Mei, Jing, Yu, Yiqin, and Syeda-Mahmood, Tanveer

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Visual Question Answering (VQA) becomes one of the most active research problems in the medical imaging domain. A well-known VQA challenge is the intrinsic diversity between the image and text modalities, and in the medical VQA task, there is another critical problem relying on the limited size of labelled image-question-answer data. In this study we propose an encoder-decoder framework that leverages the image-text joint representation learned from large-scaled medical image-caption data and adapted to the small-sized medical VQA task. The encoder embeds across the image-text dual modalities with self-attention mechanism and is independently pre-trained on the large-scaled medical image-caption dataset by multiple self-supervised learning tasks. Then the decoder is connected to the top of the encoder and fine-tuned using the small-sized medical VQA dataset. The experiment results present that our proposed method achieves better performance comparing with the baseline and SOTA methods.

Published
2023

11. Latency Guarantee for Ubiquitous Intelligence in 6G: A Network Calculus Approach

Author

Zhang, Lianming, Wang, Qian, Dong, Pingping, Wei, Yehua, and Mei, Jing

Subjects
Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing
Abstract

With the gradual deployment of 5G and the continuous popularization of edge intelligence (EI), the explosive growth of data on the edge of the network has promoted the rapid development of 6G and ubiquitous intelligence (UbiI). This article aims to explore a new method for modeling latency guarantees for UbiI in 6G given 6G's extremely stochastic nature in terahertz (THz) environments, THz channel tail behavior, and delay distribution tail characteristics generated by the UBiI random component, and to find the optimal solution that minimizes the end-to-end (E2E) delay of UbiI. In this article, the arrival curve and service curve of network calculus can well characterize the stochastic nature of wireless channels, the tail behavior of wireless systems and the E2E service curve of network calculus can model the tail characteristic of the delay distribution in UbiI. Specifically, we first propose demands and challenges facing 6G, edge computing (EC), edge deep learning (DL), and UbiI. Then, we propose the hierarchical architecture, the network model, and the service delay model of the UbiI system based on network calculus. In addition, two case studies demonstrate the usefulness and effectiveness of the network calculus approach in analyzing and modeling the latency guarantee for UbiI in 6G. Finally, future open research issues regarding the latency guarantee for UbiI in 6G are outlined.

Published
2022

17. Tetramethylpyrazine Nitrone alleviates D-galactose-induced murine skeletal muscle aging and motor deficits by activating the AMPK signaling pathway

Author

Lulin Nie, Kaiwu He, Chaoming Qiu, Qing Li, Bocheng Xiong, Chuanyue Gao, Xiufen Zhang, Mei Jing, Wei Wu, Jianjun Liu, Gaoxiao Zhang, Zaijun Zhang, Xifei Yang, Yewei Sun, and Yuqiang Wang

Subjects
Tetramethylpyrazine nitrone (TBN), Muscle aging, Motor deficits, Mitochondrial function, AMPK signaling, Therapeutics. Pharmacology, RM1-950
Abstract

Tetramethylpyrazine nitrone (TBN), a novel derivative of tetramethylpyrazine (TMP) designed and synthesized by our group, possesses multi-functional mechanisms of action and displays broad protective effects in vitro and in animal models of age-related brain disorders such as stroke, Alzheimer’s disease (AD), Amyotrophic Lateral Sclerosis (ALS) and Parkinson’s disease (PD). In the present report, we investigated the effects of TBN on aging, specifically on muscle aging and the associated decline of motor functions. Using a D-galactose-induced aging mouse model, we found that TBN could reverse the levels of several senescence and aging markers including p16, p21, ceramides, and telomere length and increase the wet-weight ratio of gastrocnemius muscle tissue, demonstrating its efficacy in ameliorating muscle aging. Additionally, the pharmacological effects of TBN on motor deficits (gait analysis, pole-climbing test and grip strength test), muscle fibrosis (hematoxylin & eosin (HE), Masson staining, and αSMA staining), inflammatory response (IL-1β, IL-6, and TNF-α), and mitochondrial function (ATP, mitochondrial membrane potential (MMP) and reactive oxygen species (ROS) were also confirmed in the D-galactose-induced aging models. Further experiments demonstrated that TBN alleviated muscle aging and improved the decline of age-related motor deficits through an AMPK-dependent mechanism. These findings highlight the significance of TBN as a potential anti-aging agent to combat the occurrence and development of aging and age-related diseases.

Published
2024
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19. Ixazomib-based frontline therapy followed by ixazomib maintenance in frail elderly newly diagnosed with multiple myeloma: a prospective multicenter studyResearch in context

Author

Li Bao, Yu-Tong Wang, Peng Liu, Min-Qiu Lu, Jun-Ling Zhuang, Mei Zhang, Zhong-Jun Xia, Zhen-Ling Li, Ying Yang, Zhen-Yu Yan, Hong-Mei Jing, Fei Dong, Wen-Ming Chen, Yin Wu, He-Bing Zhou, Rong Fu, Yu-Ping Gong, Wen-Rong Huang, and Yong-Qing Zhang

Subjects
Frail, Elderly, Multiple myeloma, All oral regimen, Quality of life, Medicine (General), R5-920
Abstract

Summary: Background: Frail elderly patients with newly diagnosed multiple myeloma (NDMM) have inferior survival and less benefit from high-dose therapies. This prospective study aimed to investigate the efficacy, safety, and quality of life (QoL) of induction treatment of ixazomib/lenalidomide/dexamethasone (IRd) and ixazomib/pegylated liposomal doxorubicin/dexamethasone (IDd) followed by ixazomib/dexamethasone (Id) maintenance therapy in frail, elderly patients with NDMM. Methods: From July 2019 to December 2021, this non-randomized concurrent controlled clinical study enrolled 120 NDMM patients aged ≥65 years with frailty defined by the International Myeloma Working Group (IMWG) frailty score or Mayo geriatric scoring system. The enrolled patients received 6–8 cycles of IRd or IDd followed by Id maintenance therapy for a minimum of 2 years at the discretion of physicians based on patient's clinical characteristics (chiCTR1900024917). Findings: The median age was 71 years and 55% of the patients were males. The overall response rate (ORR) was 82% and 77%, complete response (CR) rate was 25% and 12% for IRd and IDd groups, respectively. The difference in ORR of the Idd group minus the IRd group was −5.36% (95% CI: −18.9% to 8.19%), indicating that the ORR of the IDd group was neither inferior nor non-inferior to the IRd group. After a median follow-up of 34.3 months, the median progression-free survival (PFS) was 21.6 and 13.9 months, OS was not reached and 29.2 months in IRd and IDd groups, respectively. 28 and 33 patients discontinued induction therapy, 20 and 19 discontinued maintenance therapy in IRd and IDd groups, respectively. Cumulative Grade 3 or higher hematological adverse events (AEs) occurred in 10 of the 60 patients (17%) and non-hematological AEs occurred in 15 of the 60 patients (25%) in the IRd group, while 13 of the 60 patients (22%) and 21 of the 60 patients (35%) in the IDd group. Patients were observed with clinically significant improvement in QoL when compared with that at baseline in both IRd and IDd groups by evaluation per cycle (P

Published
2024
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25. Regional Practice Variation and Outcomes in the Standard Versus Accelerated Initiation of Renal Replacement Therapy in Acute Kidney Injury (STARRT-AKI) Trial: A Post Hoc Secondary Analysis

Author

Vaara, Suvi T., Serpa Neto, Ary, Bellomo, Rinaldo, Adhikari, Neill K. J., Dreyfuss, Didier, Gallagher, Martin, Gaudry, Stephane, Hoste, Eric, Joannidis, Michael, Pettilä, Ville, Wang, Amanda Y., Kashani, Kianoush, Wald, Ron, Bagshaw, Sean M., Ostermann, Marlies, Bagshaw, Sean M, Wald, Ron, Adhikari, Neill K.J., Bellomo, Rinaldo, Dreyfuss, Didier, Du, Bin, Gallagher, Martin P., Gaudry, Stéphane, Hoste, Eric A., Lamontagne, François, Joannidis, Michael, Liu, Kathleen D., McAuley, Daniel F., McGuinness, Shay P., Nichol, Alistair D., Ostermann, Marlies, Palevsky, Paul M., Qiu, Haibo, Pettilä, Ville, Schneider, Antoine G., Smith, Orla M., Vaara, Suvi T., Weir, Matthew, Bellomo, Rinaldo, Eastwood, Glenn M., Peck, Leah, Young, Helen, Kruger, Peter, Laurie, Gordon, Saylor, Emma, Meyer, Jason, Venz, Ellen, Wetzig, Krista, French, Craig, McGain, Forbes, Mulder, John, Fennessy, Gerard, Koottayi, Sathyajith, Bates, Samantha, Towns, Miriam, Morgan, Rebecca, Tippett, Anna, Udy, Andrew, Mason, Chris, Licari, Elisa, Gantner, Dashiell, McClure, Jason, Nichol, Alistair, McCracken, Phoebe, Board, Jasmin, Martin, Emma, Vallance, Shirley, Young, Meredith, Vladic, Chelsey, McGloughlin, Steve, Gattas, David, Buhr, Heidi, Coles, Jennifer, Hutch, Debra, Wun, James, Cole, Louise, Whitehead, Christina, Lowrey, Julie, Masters, Kristy, Gresham, Rebecca, Campbell, Victoria, Gutierrez, David, Brailsford, Jane, Forbes, Loretta, Murray, Lauren, Maguire, Teena, NiChonghaile, Martina, Orford, Neil, Bone, Allison, Elderkin, Tania, Salerno, Tania, Chimunda, Tim, Fletcher, Jason, Broadfield, Emma, Porwal, Sanjay, Knott, Cameron, Boschert, Catherine, Smith, Julie, Richardson, Angus, Hill, Dianne, Duke, Graeme, Oziemski, Peter, Cegarra, Santiago, Chan, Peter, Welsh, Deborah, Hunter, Stephanie, Roodenburg, Owen, Dyett, John, Kokotsis, Nicos, Moser, Max, Yang, Yang, Padayachee, Laven, Vetro, Joseph, Gangopadhyay, Himangsu, Kaufman, Melissa, Ghosh, Angaj, Said, Simone, Patel, Alpesh, Bihari, Shailesh, Matheson, Elisha, Jin, Xia, Shrestha, Tapaswi, Schwartz, Kate, Gallagher, Martin P., Cross, Rosalba, Cheung, Winston, Wong, Helen, Kol, Mark, Shah, Asim, Wang, Amanda Y., Endre, Zoltan, Bradford, Celia, Janin, Pierre, Finfer, Simon, Diel, Naomi, Gatward, Jonathan, Hammond, Naomi, Delaney, Anthony, Bass, Frances, Yarad, Elizabeth, Buscher, Hergen, Reynolds, Claire, Baker, Nerilee, Joannidis, Michael, Bellmann, Romuald, Peer, Andreas, Hasslacher, Julia, Koglberger, Paul, Klein, Sebastian, Zotter, Klemens, Brandtner, Anna, Finkenstedt, Armin, Ditlbacher, Adelheid, Hartig, Frank, Fries, Dietmar, Bachler, Mirjam, Schenk, Bettina, Wagner, Martin, Staudinger, Thomas, Tiller, Esther, Schellongowski, Peter, Bojic, Andja, Hoste, Eric A., Bracke, Stephanie, De Crop, Luc, Vermeiren, Daisy, Thome, Fernando, Chiella, Bianca, Fendt, Lucia, Antunes, Veronica, Maisonneuve-Rosemont, Lafrance, Jean-Philippe, Lamontagne, François, D’Aragon, Frédérick, St-Arnaud, Charles, Mayette, Michael, Carbonnaeu, Élaine, Marchand, Joannie, Masse, Marie-Hélène, Ladouceur, Marilène, Turgeon, Alexis F., Lauzier, François, Bellemare, David, Langis Francoeur, Charles, LeBlanc, Guillaume, Guilbault, Gabrielle, Grenier, Stéphanie, Cloutier, Eve, Boivin, Annick, Delisle-Thibault, Charles, Giannakouros, Panagiota, Costerousse, Olivier, Cailhier, Jean-François, Carrier, François-Martin, Ghamraoui, Ali, Lebrasseur, Martine, Benettaib, Fatna, Salamé, Maya, Boumahni, Dounia, Tung Sia, Ying, Naud, Jean-François, Roy, Isabelle, Stelfox, Henry T., Ruddell, Stacey, Manns, Braden J., Duggan, Shelley, Carney, Dominic, Barchard, Jennifer, Whitlock, Richard P., Belley-Cote, Emilie, Savija, Nevena, Sabev, Alexandra, Campbell, Troy, Creary, Thais, Devereaux, Kelson, Brodutch, Shira, Rigatto, Claudio, Paunovic, Bojan, Mooney, Owen, Glybina, Anna, Harasemiw, Oksana, Di Nella, Michelle, Harmon, John, Mehta, Navdeep, Lakatos, Louis, Haslam, Nicole, Lellouche, Francois, Simon, Mathieu, Tung, Ying, Lizotte, Patricia, Bourchard, Pierre-Alexandre, Rochwerg, Bram, Karachi, Tim, Millen, Tina, Muscedere, John, Maslove, David, Gordon Boyd, J., Sibley, Stephanie, Drover, John, Hunt, Miranda, Georgescu, Ilinca, Wax, Randy, Lenga, Ilan, Sridhar, Kavita, Steele, Andrew, Fusco, Kelly, Ghate, Taneera, Tolibas, Michael, Robinson, Holly, Weir, Matthew A., Taneja, Ravi, Ball, Ian M., Garg, Amit, Campbell, Eileen, Ovsenek, Athena, Bagshaw, Sean M., van Diepen, Sean, Baig, Nadia, Magder, Sheldon, Yao, Han, Alam, Ahsan, Campisi, Josie, MacIntyre, Erika, Rokosh, Ella, Scherr, Kimberly, Lapinsky, Stephen, Mehta, Sangeeta, Shah, Sumesh, Niven, Daniel J., Stelfox, Henry T., Ruddell, Stacey, Russell, Michael, Jim, Kym, Brown, Gillian, Oxtoby, Kerry, Hall, Adam, Benoit, Luc, Sokolowski, Colleen, Prasad, Bhanu, Rao, Jag, Giebel, Shelley, Kutsogiannis, Demetrios J., Thompson, Patricia, Thompson, Tayne, Cirone, Robert, Kavikondala, Kanthi, Soth, Mark, Clarke, France, Takaoka, Alyson, Wald, Ron, Mazer, David, Burns, Karen, Friedrich, Jan, Klein, David, Sandhu, Gyan, Santos, Marlene, Khalid, Imrana, Hodder, Jennifer, Dodek, Peter, Ayas, Najib, Alcuaz, Victoria, Suen, Gabriel, Rewa, Oleksa, Singh, Gurmeet, Norris, Sean, Gibson, Neil, Arias, Castro, Shami, Aysha, Pelletier, Celine, Adhikari, Neill K.J., Zahirieh, Alireza, Amaral, Andre, Marinoff, Nicole, Kaur, Navjot, Perez, Adic, Wang, Jane, Haljan, Gregory, Condin, Christopher, McIntyre, Lauralyn, Gomes, Brigette, Porteous, Rebecca, Watpool, Irene, Hiremath, Swapnil, Clark, Edward, Herridge, Margaret S., Backhouse, Felicity, Elizabeth Wilcox, M., Walczak, Karolina, Ki, Vincent, Sharman, Asheer, Romano, Martin, Bagshaw, Sean M., Noel Gibney, R.T., Romanovsky, Adam S., Rewa, Oleksa, McCoshen, Lorena, Baig, Nadia, Wood, Gordon, Ovakim, Daniel, Auld, Fiona, Carney, Gayle, Duan, Meili, Ji, Xiaojun, Guo, Dongchen, Qi, Zhili, Lin, Jin, Zhang, Meng, Dong, Lei, Liu, Jingfeng, Liu, Pei, Zhi, Deyuan, Bai, Guoqiang, Qiu, Yu, Yang, Ziqi, Bai, Jing, Liu, Zhuang, Zhuang, Haizhou, Wang, Haiman, Li, Jian, Zhao, Mengya, Zhou, Xiao, Shi, Xianqing, Ye, Baning, Liu, Manli, Wu, Jing, Fu, Yongjian, Long, Dali, Pan, Yu, Wang, Jinlong, Mei, Huaxian, Zhang, Songsong, Wen, Mingxiang, Yang, Enyu, Mu, Sijie, Li, Jianquan, Hu, Tingting, Qin, Bingyu, Li, Min, Wang, Cunzhen, Dong, Xin, Wang, Kaiwu, Wang, Haibo, Yang, Jianxu, Du, Bin, Wang, Chuanyao, Wang, Dongxin, Li, Nan, Yu, Zhui, Xu, Song, Yao, Lan, Hou, Guo, Liu, Zhou, Lu, Liping, Lian, Yingtao, Wang, Chunting, Zhang, Jichen, Ding, Ruiqi, Qi, Guoqing, Wang, Qizhi, Wang, Peng, Meng, Zhaoli, Chen, Man, Hu, Xiaobo, He, Xiandi, Zhao, Shibing, Hang, Lele, Li, Rui, Qin, Suhui, Lu, Kun, Dun, Shijuan, Liu, Cheng, Zhou, Qi, Chen, Zhenzhen, Mei, Jing, Zhang, Minwei, Xu, Hao, Lin, Jincan, Shi, Qindong, Fu, Lijuan, Zeng, Qinjing, Ma, Hongye, Yan, Jinqi, Gao, Lan, Liu, Hongjuan, Zhang, Lei, Li, Hao, He, Xiaona, Fan, Jingqun, Guo, Litao, Liu, Yu, Wang, Xue, Sun, Jingjing, Liu, Zhongmin, Yang, Juan, Ding, Lili, Sheng, Lulu, Liu, Xingang, Yan, Jie, Wang, Quihui, Wang, Yifeng, Zhao, Dan, Zhao, Shuangping, Hu, Chenghuan, Li, Jing, Deng, Fuxing, Qiu, Haibo, Yang, Yi, Mo, Min, Pan, Chun, Wu, Changde, Huang, Yingzi, Huang, Lili, Liu, Airan, Pettilä, Ville, Vaara, Suvi T., Korhonen, Anna-Maija, Törnblom, Sanna, Sutinen, Sari, Pettilä, Leena, Heinonen, Jonna, Lappi, Eliria, Suhonen, Taria, Karlsson, Sari, Hoppu, Sanna, Jalkanen, Ville, Kuitunen, Anne, Levoranta, Markus, Långsjö, Jaakko, Ristimäki, Sanna, Malila, Kaisa, Wootten, Anna, Varila, Simo, Järvisalo, Mikko J, Inkinen, Outi, Kentala, Satu, Leivo, Keijo, Haltia, Paivi, Dreyfuss, Didier, Ricard, Jean-Damien, Messika, Jonathan, Tiagarajah, Abirami, Emery, Malo, Dechanet, Aline, Gernez, Coralie, Roux, Damien, Martin-Lefevre, Laurent, Fiancette, Maud, Vinatier, Isabelle, Claude Lacherade, Jean, Colin, Gwenhaël, Lebert, Christine, Azais, Marie-Ange, Yehia, Aihem, Pouplet, Caroline, Henry- Lagarrigue, Matthieu, Seguin, Amélie, Crosby, Laura, Maizel, Julien, Titeca-Beauport, Dimitri, Combes, Alain, Nieszkowska, Ania, Masi, Paul, Demoule, Alexandre, Mayaux, Julien, Dres, Martin, Morawiec, Elise, Decalvele, Maxens, Demiri, Suela, Faure, Morgane, Marios, Clémence, Mallet, Maxime, Amélie Ordon, Marie, Morizot, Laura, Cantien, Marie, Pousset, François, Gaudry, Stéphane, Poirson, Florent, Cohen, Yves, Argaud, Laurent, Cour, Martin, Bitker, Laurent, Simon, Marie, Hernu, Romain, Baudry, Thomas, De La Salle, Sylvie, Robine, Adrien, Sedillot, Nicholas, Tchenio, Xavier, Bouisse, Camille, Roux, Sylvie, Barbar, Davide, Trusson, Rémi, Tamion, Fabienne, Grangé, Steven, Carpentier, Dorothée, Chevrel, Guillaume, Ensenyat-Martin, Luis, Marque, Sophie, Quenot, Jean-Pierre, Andreu, Pascal, Dargent, Auguste, Large, Audrey, Chudeau, Nicolas, Landais, Mickael, Derrien, Benoit, Christophe Callahan, Jean, Guitton, Christophe, Le Moal, Charlène, Robert, Alain, Asehnoune, Karim, Cinotti, Raphaël, Grillot, Nicolas, Demeure, Dominique, Vinsonneau, Christophe, Rahmani, Imen, Marzouk, Mehdi, Dekeyser, Thibault, Sejourne, Caroline, Verlay, Mélanie, Thevenin, Fabienne, Delecolle, Lucie, Didier Thevenin, Lens, Souweine, Bertrand, Coupez, Elisabeth, Adda, Mireille, Eraldi, Jean-Pierre, Marchalot, Antoine, De Prost, Nicolas, Mekontso Dessap, Armand, Razazi, Keyvan, Meziani, Ferhat, Boisrame-Helms, Julie, Clere-Jehl, Raphael, Delabranche, Xavier, Kummerlen, Christine, Merdji, Hamid, Monnier, Alexandra, Rabouel, Yannick, Rahmani, Hassene, Allam, Hayat, Chenaf, Samir, Franja, Vincenta, Pons, Bertrand, Carles, Michel, Martino, Frédéric, Richard, Régine, Zuber, Benjamin, Lacave, Guillaume, Lakhal, Karim, Rozec, Bertrand, Dang Van, Hoa, Boulet, Éric, Dubos, René, Fadel, Fouad, Cleophax, Cedric, Dufour, Nicolas, Grant, Caroline, Thuong, Marie, Reignier, Jean, Canet, Emmanuel, Nicolet, Laurent, Boulain, Thierry, Nay, Mai-Anh, Benzekri, Dalila, Barbier, François, Bretagnol, Anne, Kamel, Toufik, Mathonnet, Armelle, Muller, Grégoire, Skarzynski, Marie, Rossi, Julie, Pradet, Amandine, Dos Santos, Sandra, Guery, Aurore, Muller, Lucie, Felix, Luis, Bohé, Julien, Thiéry, Guillaume, Aissaoui, Nadia, Vimpere, Damien, Commeureuc, Morgane, Diehl, Jean-Luc, Guerot, Emmanuel, Liangos, Orfeas, Wittig, Monika, Zarbock, Alexander, Küllmar, Mira, van Waegeningh, Thomas, Rosenow, Nadine, Nichol, Alistair D., Brickell, Kathy, Doran, Peter, Murray, Patrick T., Landoni, Giovanni, Lembo, Rosalba, Zangrillo, Alberto, Monti, Giacomo, Tozzi, Margherita, Marzaroli, Matteo, Lombardi, Gaetano, Paternoster, Gianluca, Vitiello, Michelangelo, McGuinness, Shay, Parke, Rachael, Butler, Magdalena, Gilder, Eileen, Cowdrey, Keri-Anne, Wallace, Samantha, Hallion, Jane, Woolett, Melissa, Neal, Philippa, Duffy, Karina, Long, Stephanie, McArthur, Colin, Simmonds, Catherine, Chen, Yan, McConnochie, Rachael, Newby, Lynette, Knight, David, Henderson, Seton, Mehrtens, Jan, Morgan, Stacey, Morris, Anna, Vander Hayden, Kymbalee, Burke, Tara, Bailey, Matthew, Freebairn, Ross, Chadwick, Lesley, Park, Penelope, Rolls, Christine, Thomas, Liz, Buehner, Ulrike, Williams, Erin, Albrett, Jonathan, Kirkham, Simon, Jackson, Carolyn, Browne, Troy, Goodson, Jennifer, Jackson, David, Houghton, James, Callender, Owen, Higson, Vicki, Keet, Owen, Dominy, Clive, Young, Paul, Hunt, Anna, Judd, Harriet, Lawrence, Cassie, Olatunji, Shaanti, Robertson, Yvonne, Latimer-Bell, Charlotte, Hendry, Deborah, Mckay-Vucago, Agnes, Beehre, Nina, Lesona, Eden, Navarra, Leanlove, Robinson, Chelsea, Jang, Ryan, Junge, Andrea, Lambert, Bridget, Schneider, Antoine G., Thibault, Michel, Eckert, Philippe, Kissling, Sébastien, Polychronopoulos, Erietta, Poli, Elettra, Altarelli, Marco, Schnorf, Madeleine, Abed Mallaird, Samia, Heidegger, Claudia, Perret, Aurelie, Montillier, Philippe, Sangla, Frederic, Neils, Seigenthaller, De Watteville, Aude, Phull, Mandeep-Kaur, George, Aparna, Hussain, Nauman, Pogreban, Tatiana, Lobaz, Steve, Daniels, Alison, Cunningham, Mishell, Kerr, Deborah, Nicholson, Alice, Shanmugasundaram, Pradeep, Abrams, Judith, Manso, Katarina, Hambrook, Geraldine, McKerrow, Elizabeth, Salva, Juvy, Foulkes, Stephen, Wise, Matthew, Morgan, Matt, Brooks, Jenny, Cole, Jade, Michelle Davies, Tracy, Hill, Helen, Thomas, Emma, Vizcaychipi, Marcela, Baharlo, Behrad, Carungcong, Jaime, Costa, Patricia, Martins, Laura, Kapoor, Ritoo, Hazelton, Tracy, Moon, Angela, Musselwhite, Janine, Shelley, Ben, McCall, Philip, Ostermann, Marlies, Arbane, Gill, Bociek, Aneta, Marotti, Martina, Lim, Rosario, Campos, Sara, Grau Novellas, Neus, Cennamo, Armando, Slack, Andrew, Wyncoll, Duncan, Camporota, Luigi, Sparkes, Simon, Tilley, Rosalinde, Rattray, Austin, Moreland, Gayle, Duffy, Jane, McGonigal, Elizabeth, Hopkins, Philip, Finney, Clare, Smith, John, Noble, Harriet, Watson, Hayley, Harris, Claire-Louise, Clarey, Emma, Corcoran, Eleanor, Beck, James, Howcroft, Clare, Youngs, Nora, Wilby, Elizabeth, Ogg, Bethan, Wolverson, Adam, Lee, Sandra, Butler, Susie, Okubanjo, Maryanne, Hindle, Julia, Welters, Ingeborg, Williams, Karen, Johnson, Emily, Patrick-Heselton, Julie, Shaw, David, Waugh, Victoria, Stewart, Richard, Mwaura, Esther, Wren, Lynn, Mew, Louise, Sutherland, Sara-Beth, Adderley, Jane, Ruddy, Jim, Harkins, Margaret, Kaye, Callum, Scott, Teresa, Mitchell, Wendy, Anderson, Felicity, Willox, Fiona, Jagannathan, Vijay, Clark, Michele, Purv, Sarah, Sharman, Andrew, Meredith, Megan, Ryan, Lucy, Conner, Louise, Peters, Cecilia, Harvey, Dan, Roshdy, Ashraf, Collins, Amy, Sim, Malcolm, Henderson, Steven, Chee, Nigel, Pitts, Sally, Bowman, Katie, Dilawershah, Maria, Vamplew, Luke, Howe, Elizabeth, Rogers, Paula, Hernandez, Clara, Prendergast, Clara, Benton, Jane, Rosenberg, Alex, Forni, Lui G., Grant, Alice, Carvelli, Paula, Raithatha, Ajay, Bird, Sarah, Richardson, Max, Needham, Matthew, Hirst, Claire, Ball, Jonathan, Leaver, Susannah, Howlett, Luisa, Castro Delgado, Carlos, Farnell-Ward, Sarah, Farrah, Helen, Gray, Geraldine, Joseph, Gipsy, Robinson, Francesca, Tridente, Ascanio, Harrop, Clare, Shuker, Karen, McLaughlan, Derek, Ramsey, Judith, Meehan, Sharon, Oliver Rose, Bernd, Reece-Anthony, Rosie, Gurung, Babita, Whitehouse, Tony, Snelson, Catherine, Veenith, Tonny, Johnston, Andy, Cooper, Lauren, Carrera, Ron, Ellis, Karen, Fellows, Emma, Harkett, Samanth, Bergin, Colin, Spruce, Elaine, Despy, Liesl, Goundry, Stephanie, Dooley, Natalie, Mason, Tracy, Clark, Amy, Dignam, Gemma, Ward, Geraldine, Attwood, Ben, Parsons, Penny, Mason, Sophie, Margarson, Michael, Lord, Jenny, McGlone, Philip, Hodgson, Luke E., Chadbourn, Indra, Gomez, Raquel, Margalef, Jordi, Pretorius, Rinus, Hamshere, Alexandra, Carter, Joseph, Cahill, Hazel, Grainger, Lia, Howard, Kate, Forshaw, Greg, Guy, Zoe, Kashani, Kianoush B., Albright, Robert C., Amsbaugh, Amy, Stoltenberg, Anita, Niven, Alexander S., Lynch, Matthew, O’Mara, AnnMarie, Naeem, Syed, Sharif, Sairah, McKenney Goulart, Joyce, Lynch, Matthew, O’Mara, AnnMarie, Naeem, Syed, Sharif, Sairah, McKenney Goulart, Joyce, Tolwani, Ashita, Lyas, Claretha, Latta, Laura, Bihorac, Azra, Hashemighouchani, Haleh, Efron, Philip, Ruppert, Matthew, Cupka, Julie, Kiley, Sean, Carson, Joshua, White, Peggy, Omalay, George, Brown, Sherry, Velez, Laura, Marceron, Alina, Neyra, Javier A., Carlos Aycinena, Juan, Elias, Madona, Ortiz-Soriano, Victor M., Hauschild, Caroline, and Dorfman, Robert

Published
2024
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37. Low-dose decitabine for previously untreated acute myeloid leukemia ineligible for intensive chemotherapy aged 65 years or older: a prospective study based on comprehensive geriatric assessment

Author

Ru Feng, Shuai Zhang, Jiang-Tao Li, Ting Wang, Chun-Li Zhang, Jie-Fei Bai, Lei Yang, Li-Ru Wang, Hong-Mei Jing, and Hui Liu

Subjects
Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Background: The outcome of patients with acute myeloid leukemia (AML) aged ⩾65 years is poor. Effective treatment options are limited for patients with AML who cannot tolerate intensive chemotherapy. Objectives: We aimed to evaluate the efficacy of low-dose decitabine in previously untreated patients with AML aged ⩾65 years who were ineligible for intensive chemotherapy based on a comprehensive geriatric assessment. Design: We performed a prospective, multicenter, open-label, and non-randomized study. Methods: Patients were enrolled at four centers in Beijing between 1 January 2017 and 31 December 2020. They were treated with decitabine at a dose of 6 mg/m 2 for 10 days. The treatment was repeated every 28 days for one cycle for a total of six cycles. The primary endpoint of our study was overall survival (OS) at the end of the first year after enrolment. The secondary endpoints included overall response rate, leukemia-free survival, relapse rate, treatment-related mortality (TRM), quality of life, safety, and transfusion dependence. Patients were continuously monitored for toxicity. Results: Overall, 47 patients (30 males and 17 females) participated in this study. The median age of the enrolled patients was 78 (range, 65−90) years. The median follow-up time was 22.2 (range, 4.6−38.8) months. Fifteen (31.9%) patients achieved complete remission (CR), 11 (23.4%) patients achieved partial remission, 3 (6.4%) patients achieved hematological improvement only, and 18 (38.3%) patients did not achieve remission. The median time to obtain CR was 2 months. The median CR was 8.5 months. Of the patients, 36 (76.6%) patients completed six cycles of treatment with low-dose decitabine, and the 1-year OS was 36.1%. According to instrumental activities of daily living scales, age, comorbidities, and albumin (IACA) scores, the median survival was 11.2 months in the unfit group and 6 months in the frail group. The 1-year OS rates in the unfit and frail groups were 49.2% and 23.4%, respectively. Grade ⩾3 non-hematological toxicity was observed in 70.2% (33/47) of the patients. TRM occurred in three patients. No early deaths occurred after treatment. Conclusion: In newly diagnosed older patients with AML whose IACA assessment was unfit or frail for standard chemotherapy, treatment with low-dose decitabine demonstrated clinical activity and good security in our study.

Published
2023
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38. Real‐world treatment and outcome patterns of patients with mantle cell lymphoma in China: A large, multicenter retrospective analysis

Author

Ping Yang, Qing‐qing Cai, Wei Zhang, Shuo‐zi Liu, Hui Liu, Xiu‐hua Sun, Yu‐jun Dong, Xiu‐bin Xiao, Jing‐wen Wang, Zhen‐ling Li, Wen‐rong Huang, Li‐hong Li, Hui‐zheng Bao, Wei Yang, Ya‐lan Wang, Shu‐ye Wang, Juan He, Xiao‐ling Li, Ai‐chun Liu, and Hong‐mei Jing

Subjects
high‐dose cytarabine, maintenance treatment, mantle cell lymphoma, relapse/refractory treatment, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Mantle cell lymphoma (MCL) is an uncommon heterogeneous subtype of B cell non‐Hodgkin lymphoma, and clinical features in MCL appear regional characteristics. MCL treatment opinions are not uniform between countries or regions within Asia and China, and Asian patient‐specific data for MCL treatment are fewer. The study aims to explore the clinical characteristics, treatment patterns and prognosis of MCL patients in China. Methods A total of 805 patients diagnosed with MCL between April 1999 and December 2019 at 19 comprehensive hospitals in China were included in this retrospective analysis. Kaplan‐Meier method coupled with the log‐rank test was used for univariate analysis, and COX proportional hazards model was used for multivariate analysis (MVA). p < 0.05 was consided statistically significant. All outputs were produced using R version 4.1.0. Results The median age of the cohort was 60.0 years with a male‐to‐female ratio of 3.36:1. Five‐year progression‐free survival (PFS) and overall survival (OS) rates were 30.9% and 65.0%, respectively. High‐intermediate/high‐risk group according to MIPI‐c, without high‐dose cytarabine, lack of Auto‐SCT as consolidation and maintenance treatment and SD/PD in initial treatment remained statistically relevant to poor PFS on MVA, and ki67 ≥50%, B symptoms, high‐intermediate/high risk group according to MIPI‐c, without high‐dose cytarabine, lack of maintenance treatment, SD/PD in initial treatment and relapse/refractory state were independently associated with poorer OS on MVA. Conclusions First‐line high dose cytarabine exposure, auto‐SCT as consolidation therapy obtained survival benefits in Chinese population. Our study further confirmed the value of maintenance treatment and explored the application of new drug treatment and bendamustine in R/R MCL patients.

Published
2023
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40. A complete gap-free diploid genome in Saccharum complex and the genomic footprints of evolution in the highly polyploid Saccharum genus

Author

Wang, Tianyou, Wang, Baiyu, Hua, Xiuting, Tang, Haibao, Zhang, Zeyu, Gao, Ruiting, Qi, Yiying, Zhang, Qing, Wang, Gang, Yu, Zehuai, Huang, Yongji, Zhang, Zhe, Mei, Jing, Wang, Yuhao, Zhang, Yixing, Li, Yihan, Meng, Xue, Wang, Yongjun, Pan, Haoran, Chen, Shuqi, Li, Zhen, Shi, Huihong, Liu, Xinlong, Deng, Zuhu, Chen, Baoshan, Zhang, Muqing, Gu, Lianfeng, Wang, Jianping, Ming, Ray, Yao, Wei, and Zhang, Jisen

Published
2023
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43. Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records

Author

Yu, Yiqin, Chen, Pin-Yu, Zhou, Yuan, and Mei, Jing

Subjects
Computer Science - Machine Learning
Abstract

With the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems. However, due to the heterogeneity of EHRs, models trained on different patient groups suffer from poor generalizability. How to mitigate domain shifts between the source patient group where the model is built upon and the target one where the model will be deployed becomes a critical issue. In this paper, we propose a data augmentation method to facilitate domain adaptation, which leverages knowledge from the source patient group when training model on the target one. Specifically, adversarially generated samples are used during domain adaptation to fill the generalization gap between the two patient groups. The proposed method is evaluated by a case study on different predictive modeling tasks on MIMIC-III EHR dataset. Results confirm the effectiveness of our method and the generality on different tasks.

Published
2021

45. Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning

Author

Yu, Yiqin, Min, Xu, Zhao, Shiwan, Mei, Jing, Wang, Fei, Li, Dongsheng, Ng, Kenney, and Li, Shaochun

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions. At the same time, the available model is often proprietary, i.e., neither the model parameter nor the data set used for model training is accessible. In consequence, leveraging the knowledge hidden in the available model (aka. the hypothesis) and adapting it to a local data set becomes extremely challenging. Motivated by this situation, in this paper we aim to address such a specific case within the hypothesis transfer learning framework, in which 1) the source hypothesis is a black-box model and 2) the source domain data is unavailable. In particular, we introduce a novel algorithm called dynamic knowledge distillation for hypothesis transfer learning (dkdHTL). In this method, we use knowledge distillation with instance-wise weighting mechanism to adaptively transfer the "dark" knowledge from the source hypothesis to the target domain.The weighting coefficients of the distillation loss and the standard loss are determined by the consistency between the predicted probability of the source hypothesis and the target ground-truth label.Empirical results on both transfer learning benchmark datasets and a healthcare dataset demonstrate the effectiveness of our method., Comment: 7 pages, 2 figures

Published
2020

46. Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification

Author

Zhang, Tengteng, Yu, Yiqin, Mei, Jing, Tang, Zefang, Zhang, Xiang, and Li, Shaochun

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract sentences from medical literature and classify them into each class. However, in most circumstances, there will be more than one evidences in an extracted sentence even it has been categorized to a certain class. In order to address this problem, we propose a step-wise disease Named Entity Recognition (DNER) extraction and PICO identification method. With our method, sentences in paper title and abstract are first classified into different classes of PICO, and medical entities are then identified and classified into P and O. Different kinds of deep learning frameworks are used and experimental results show that our method will achieve high performance and fine-grained extraction results comparing with conventional PICO extraction works., Comment: 9 pages, 3 figures

Published
2020

48. Functionalization of polyethylene terephthalate fabrics with au@Cu2O core@shell nanocrystals for environmental purifications

Author

Jhen-Yang Wu, Mei-Jing Fang, Tomoyuki Kurioka, Ting-Hsuan Lai, Ming-Yu Kuo, Yi-Hsuan Chiu, Chun-Wen Tsao, Yi-An Chen, Hsuan-Hung Kuo, Yu-An Chien, Po-Wei Cheng, Bo-You Lin, Sue-Min Chang, Chun-Yi Chen, Masato Sone, Tso-Fu Mark Chang, and Yung-Jung Hsu

Subjects
Functional textiles, Polyethylene terephthalate fabrics, Au@Cu2O, Core@shell nanocrystals, Photocatalysis, Electronics, TK7800-8360, Technology (General), T1-995
Abstract

Wastewater containing synthetic dyes has caused a significant risk to the environment and human health. Among the various methods to treat wastewater, photocatalysis recommends itself as a particularly efficient tool for the removal of dyes from industrial effluents. In this work, Au@Cu2O core@shell nanocrystals with controllable shell thicknesses from 38.1 ± 2.8 (Au@Cu2O-2), 48.1 ± 3.7 (Au@Cu2O-3) to 59.1 ± 4.1 nm (Au@Cu2O-4) have been prepared and immobilized on polyethylene terephthalate (PET) fabrics for applications in photocatalytic degradation of methylene orange (MO). The influence of the shell thickness of Au@Cu2O on the photocatalytic performance of the functionalized PET fabrics has been examined. Among all the samples tested, immobilization of Au@Cu2O-3 rendered PET fabrics the largest photocatalytic activity for MO degradation, achieving an apparent rate constant of MO degradation of 7.43 × 10−3 min−1. A plausible mechanism accounting for the degradation process of MO over the functionalized PET has been proposed based on the results of scavenger experiments. This work has provided a delicate yet practical functional textile paradigm by combining the photocatalytic capability of Au@Cu2O and the adaptable feature of PET fabrics. The findings from this study can deliver a viable idea for the design of versatile textiles with competent photocatalytic capacity for environmental purifications and energy conversion.

Published
2023
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49. A relationship of porosity and mechanical properties of spark plasma sintered scandia stabilized zirconia thermal barrier coating

Author

AKM Asif Iqbal and Mei Jing Lim

Subjects
Scandia stabilized zirconia (ScSZ), Spark plasma sintering (SPS), Thermal barrier coating (TBC), Porosity, Mechanical properties, Technology
Abstract

Porous ceramic materials are popularly accepted as thermal barrier coatings (TBCs) in insulating gas turbine parts working at high temperatures. In this research, three different types of Scandia stabilized zirconia (ScSZ) systems, a nanometric 10 mol% ScSZ (10-ScSZ), a micrometric 8 mol% ScSZ (8-ScSZ) and a combination of these two powders (10-8-ScSZ) have been developed by Spark Plasma Sintering (SPS) process. Varying SPS parameters, for instance, temperature, pressure and dwell time were applied to develop the different volumes of porosity in the materials. Subsequently, the microstructure of the materials has been studied and mechanical properties have been evaluated. All three materials demonstrate a reduced porosity level at high sintering temperature and pressure. However, the nanometric 10-ScSZ material shows a higher reduction of porosity from 51.8% to 11.1% at 30 MPa pressure and 40.2%–8.5% at 60 MPa pressure within the temperature range of 1000–1200 °C. Besides, the 10-8-ScSZ composite exhibits substantially increased porosity in comparison to its constituent parts. The results also show that the nanometric 10-ScSZ material exhibits a greater mechanical strength including Vickers microhardness of 81 HV, flexural strength of 361 MPa and elastic modulus of 187 GPa at 5% porosity level, as compared to the other two materials. Additionally, it is observed that all the mechanical properties for all three materials consistently decrease with the increase in porosity levels. While compared with the traditional atmospheric plasma spray (APS) processed ceramic coating, the porous ScSZ coating materials exhibit a larger elastic modulus. Therefore, the porous ScSZ developed by the SPS process could be a prospective alternative thermal barrier coating (TBC).

Published
2023
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