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8. Research on Matching Algorithm of Color

Author

Cao, Yongjun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Dhar, Sourav, editor, Do, Dinh-Thuan, editor, Sur, Samarendra Nath, editor, and Liu, Chuan-Ming, editor

Published
2023
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12. The third Intensive Care Bundle with Blood Pressure Reduction in Acute Cerebral Haemorrhage Trial (INTERACT3): an international, stepped wedge cluster randomised controlled trial

Author

Robinson, Thompson, Miranda, J. Jaime, Anderson, Craig S., You, Chao, Song, Lili, Parry-Jones, Adrian, Sprigg, Nikola, Durrans, Sophie, Harris, Caroline, Bamford, Ann, Smith, Olivia, Herbert, Robert, Chen, Christopher, Whiteley, William, Hu, Rong, Billot, Laurent, Li, Qiang, Mysore, Jayanthi, Hu, Xin, Zhang, Yao, Liu, Feifeng, Sakamoto, Yuki, You, Shoujiang, Han, Qiao, Crutzen, Bernard, Li, Yunke, Cheung, Emily, Jan, Stephen, Liu, Hueiming, Ouyang, Menglu, Sun, Lingli, Chu, Honglin, Anjum, Anila, Gonzalez Mc Cawley, Francisca, Del Rio, Alejandra, Rimoli, Bruna, Cerantola, Rodrigo, Jeevarajah, Thanushanthan, Kannangara, Madhushani, Joseph, Andrene, Nanayakkara, Chamath, Chen, Xiaoying, Malavera, Alejandra, Zhang, Chunmiao, Yang, Zhao, Li, Brook, Meng, Zhuo, Liu, Leibo, Ning, Yi, Dong, Le, Armenis, Manuela, Lim, Joyce, Monaghan, Helen, Ma, Lu, Li, Xi, Luo, Rui, Cheng, Guojuan, Dong, Yilin, Liu, Ziqin, Wang, Shuihong, Zhang, Ying, Cheng, Jipeng, Shi, Hui, Li, Wenjing, Mou, Langming, Yi, Ping, Chen, Chen, Chen, Xue, Weerawardena, Shalomi, Ellawala, Poornima, Ranasinghe, Enalee, Rodrigo, Chrishmi, Wahab, Kolawala, Adeniyi, Sunday, Pandian, Jeyaraj, Khanna, Megha, Muñoz Venturelli, Paula, González, Francisca, Urrutia Goldsack, Francisca, Wasay, Mohammad, Begum, Dilshad, Pontes-Neto, Octavio, Camilo, Millene, Dias, Francisco, Vincenzi, Octavio, Moro, Carla, Santos, Renata, Texeira, Nara, Longo, Alexandre, Liberato, Rafaela, Martins, Sheila, Pille, Arthur, Chwal, Bruna, Silva, Isabel, Titton, Natacha, Weiss, Gustavo, Mora, Daissy, Ouriques, Magda, Carbonera, Leonardo, Bazan, Rodrigo, Modolo, Gabriel, Winckler, Fernanda, Miranda, Luana, Souza, Juli, Rojo, Alexis, Uslar, Wilhelm, Medel, Lorena, Lopez, Javiera, Herrero, Diego, Lavados, Pablo, Vargas Latorre, Barbara, Conejan, Nathalie, Esparza, Tomas, Sotomayor, Patricio, Wenger, Denisse, Gigoux, Juan Pablo, Letelier, Aldo, Acevedo, Lilian, Moya, Vivianne, Figueroa, Cristian, Vallejos, Nicol, Guerrero, Rodrigo, Velasquez, Mauricio, Vallejos, Jose, Pallauta, Kimerly, Santibanez, Tamara, Queirolo, Angelo, Lobos, Andrea, Jiang, Yongming, Li, Weimin, Huang, Wei, Luo, Ke, Liu, Gangying, Tang, Guanghai, Yang, Guang, Jiang, Hongtao, Zhang, Xu, Jing, Hongyan, Zhu, Sheng, Pu, Bo, Lv, Dong, Kang, Hui, Hu, Qiuping, She, Xiaochun, Jiang, Xiaoming, Chen, Yanli, Yang, Shenghua, He, Jianjun, Li, Zongping, Cheng, Gang, Huang, Hailin, Wang, Xiaoyi, Lin, Jianqiong, Chen, Minhui, Yang, Chenghao, Ding, Hao, Deng, Yunliang, Luo, Fei, Zhang, Rongjun, Wang, Xiaofeng, Zhang, Hongbing, Yang, Xiaoliang, Zhang, Yang, Yang, Chengyi, He, Yu, Liu, Feng, Wang, Rongjie, Zhang, Yuhui, Xin, Xiaodong, Feng, Bin, Hao, Wanru, Song, Chang, Guo, Yun, Jiang, Dehua, Chen, Jie, Tang, Changtong, Zhu, Hongliang, Li, Xin, Cui, Jin, Xu, Haidong, Li, Boyang, Tang, Fusheng, Li, Yuanbin, Gao, Min, Yang, Bo, Xu, Xuejun, Deng, Bing, Zheng, Yi, Ge, Yuanhong, Chen, Keyu, Liu, Yang, Li, Xinshen, Zhong, Tingting, Xu, Jianfeng, Zhang, Hai, Wang, Jiyue, Zhu, Jianxin, Sun, Hanyu, Yu, Fuhua, Zhang, Xueguang, Xu, Jianguo, Zhang, Mingsen, Wang, Bin, Ma, Yiming, Jiang, Donglin, Zhou, Jun, Liu, Cong, Nie, Wenhong, Li, Mingguo, Tian, Tao, Li, Yong, He, Mingfang, Tu, Xiaolong, Wu, Zhengjun, Liu, Hong, Zhong, Dongsheng, Jiang, Rongcai, Sun, Jian, Tian, Ye, Wei, Yingsheng, An, Shuo, Wei, Pingbo, Luo, Le, Lin, Bin, Liu, Gang, Wen, Yan, Cai, Qiang, Chen, Qianxue, Lei, Pan, Li, Zhiyang, Zhang, Meifang, He, Jiaquan, Chen, Yan, Liu, Jun, Liu, Xinghai, Li, Junyan, Chen, Min, Wang, Jing, Zhou, Bingzhi, Ye, Baichun, Zhang, Jiancheng, Zhang, Manyuan, Pan, Xuming, Yu, Xiaoxiang, Xu, Jian, Xiao, Qingbao, Wang, Yuefei, Tao, Liang, Shi, Lin, Zheng, Niandong, You, Guoliang, Lei, Bo, Chen, Shu, Wu, Honggang, Hu, Jin, Zhao, Jianlan, Yu, Jian, Yuan, Qiang, Du, Zhuoying, Tang, Xielin, Li, Qianke, Liu, Shenghua, Yang, Feilong, Xiao, Kui, Luo, Chao, Wang, Guang, Che, Xudong, Teng, Zhipeng, Wan, Wenwu, Li, Jun, Liu, Yu, Fan, Mingbo, Zhang, Tao, Cai, Lun, Ma, Yuan, Ma, Zhifeng, Li, Bin, He, Linlin, Li, Jinghui, Zhang, Weibing, Zhang, Shuxin, Zhang, Hongzhen, Dai, Yingguang, Lei, Jun, Mao, Lei, Huang, Yiyang, Zhou, Zhi, Chen, Ping, Chen, Fang, Wei, Pan, Li, Tiangui, Chen, Honglin, Zeng, Mengfei, Mou, Kejie, Xue, Jun, Jiang, Yong, Tang, Xiaoping, Chen, Tao, Zhang, Yalan, Xu, Yanbing, Gu, Yuchen, Chen, Lei, Zhao, Yujun, Yang, Bin, Kuai, Peng, Wang, Xi, Yang, Yuwang, Hu, Xueling, Zhang, Huitian, Yang, Yintao, Wang, Weifeng, Zhang, Junyi, Cheng, Wei, Zhang, Xiaoxue, Ma, Xiaowen, He, Qin, Zhang, Li, Gao, Rong, Liu, Huixiang, Ye, Jingwei, Xu, Ping, Wu, Xin, Yuan, Yuan, Zou, Peng, Zhang, Zhen, Cheng, Jiyong, Zhou, Zhangming, Zeng, Yijun, Liang, Zhang, Du, Deming, Yu, Shui, Cao, Yongjun, Xu, Jiaping, Huang, Zhichao, Chen, Dongqin, Xiao, Wenfeng, Zhu, Li, Yuan, Miao, Wang, Yuhai, Shi, Dongliang, Hu, Xu, Xiang, Dingchao, Shi, Like, Wang, Hongqin, Yang, Liu, Miao, Wang, Hu, Yiyi, Zhao, Yuchun, Hu, Xi, Zhou, Weiduo, Sun, Chao, Tang, Dong, Yao, Kun, You, Jin, Chen, Shishi, Yao, Jianmin, Li, Huanmei, Liu, Jinmei, Bai, Ailin, Yi, Yong, Deng, Qingshan, Luo, Peng, Wang, Han, Jiang, Jingcheng, Yang, Qingwei, He, Shunpo, Wang, Jun, Chen, Yu, He, Hua, Deng, Yuyang, Cao, Zhikai, Yi, Xuxia, Luo, Jinbiao, Luo, Shuang, Gong, Min, Liu, Li, Gao, Xuejun, Liu, Jia, Wu, Li'e, Zhang, Jia, Sun, Hongying, Li, Xinhui, Jia, Lu, Wu, Jianbing, Zhang, Jie, Zhang, Huajun, Du, Chunfu, Li, Shun, Yang, Xiaobin, He, Jie, Liao, Lei, Zhou, Gezhi, Dong, Wentao, Chen, Yunxiang, Lin, Xiaofeng, Shui, Xujian, Zhang, Peng, Zhao, Yuan, Yang, Hongli, Zhao, Wenbin, Zhang, Xiaoyi, Chen, Jincao, Wu, Qian, Dai, Xuan, Tang, Baogui, Wang, Yinjuan, Liu, Tao, Zhang, Haixia, Duan, Faliang, Luo, Ming, Jiao, Qingfang, Lei, Guoliang, Wang, Dong, Song, Chunwang, Tan, Haopeng, Ye, Feng, Qin, Xinghu, Liang, Xiaolong, Liu, Junling, Yang, Lang, Yang, Jie, Lin, Yapeng, Yang, Qian, Ma, Xuntai, Qi, Yinkuang, Pan, Baogen, Jiang, Caixia, Ye, Zhanying, Dong, Ce, Yue, Xiongfei, Yang, Xiaopeng, Maimaitiyiming, Tuoheti, Dong, Jun, Wu, Yonggang, Gao, Feng, Zhao, Deqiang, Zhang, Xinghai, Wang, PengJun, Jiang, Hongbo, Li, Jianping, Zhang, Wei, Chen, Jing, Tong, Haibo, Wang, Yonghong, Qiao, Kaipeng, Guo, Fuyou, Zhang, Mingchu, Hu, Yan, Feng, Mengzhao, Song, Dengpan, Zuo, Yi, Chen, Shangjun, Qian, Chao, Li, Baoming, Ma, Jingku, Zhang, Sunfu, Kong, Bin, Dong, Xingyu, Fang, Sheng, Lu, Bin, Li, Yang, Yang, Yongling, Yu, Hong, Sun, Huaiyu, Wang, Yue, Wang, Weimin, Li, Tong, Li, Shengli, Xu, Zhiming, Wang, Yongyi, Dong, Qiang, Tang, Yuping, Chu, Heling, Lu, Ying, Wang, Zhong, Sun, Xiaoou, Zhao, Jianhua, Yang, Shuaifeng, Qian, Xiying, Saroja, Aralikatte Onkarappa, Naik k, Ravishankar, Chindhi, Sandip, Pampaniya, Nakul, Amaresh, Kurubara, Iype, Thomas, R, Dileep, Rajan, Reeja, Panicker, Praveen, Das, Rupjyoti, Choudhury, Nupur, Gohain, Pankaja, Webster, Jemin, Pakma, Biyol, Sangi, Lalbiak, Sebastian, Ivy, Aggrawal, Gaurav, Raj, Komal, Rajoura, Deepankshi, Singh, Sulena, Aggrawal, Varun, Narang, Amit, Arauz, Antonio, Cano-Nigenda, Vanesa, López-Mena, Diego, Valdez-Ruvalcaba, Héctor, Toledo-Treviño, Roberto, Obiako, Reginald, Abubakar, Sani, Emeka, Oguike, Olayemi, Balogun, Lois, Melika, Philip, Ibinaiye, Comfort O, Olurishe, Okubadejo, Njideka, Agabi, Osigwe, Ojo, Oluwadamilola, Wahab, Kolawole, Bello, Abiodun, Ibukun, Oyinloye, Sanayaolu, Olufemi, Jimoh, Abdulraheem, Waheed, Shahid, Kamal, Dr.Ayeesha, Shoaib, Raja Farhat, Orooj, Fizza, Majid, Sadaf, Zehra, Taskeen, Khan, Abdus Salam, Shanker, Ravi, Syed, Nadir Ali, Ahmad, Nashwa, Abanto, Carlos, Valencia, Ana, Barrientos, Danny, Ramirez, Jorge, Calle, Pilar, Palliyeguruge, Dilum, Muthucumarana, Sumudu, Ratnayaka, Shiroma, Ganihiarachchi, Dilhara, Bandaranayake, Arundathi, Somaratne, S.D.B, Narayana, Saumya, Gallage, Sithara, Senanayake, Bimsara, Samarasiri, Udari, Luke, Dunya, Sivapathasundaram, Mythily, Sahadevan, Vithoosan, Rasmi, Amani, Deshaka, Yuran, Fernando, Nilukshi, Munasinghe, Aruna, Rathnapriya, Kapilanga, Nissanka, A.S, Karunathilake, Kanchana, Gayan, Isuru, Wijenayake, Kaminda, Gunasekara, Hasitha, Vidyarathne, Jagath, Keshavaraj, Ajantha, Janarthanan, Kanagasabapathy, Gerald Jeevathasan, Arhivalaky, Sivamainthan, Sivaram, John Priyanth, Mathyamuthan, John Priyanth, Abirami, Rajendiran, Thambippillai, Alwis, Sanjeewa, Gunasekare, Nushara, Liyanarachchi, Vasundara, Dissanayake, Athula, Uluwattage, Wimalasiri Mewa, Ratnayake, Gimhani, Rajinee, Charika, Jayawardana, Sakura, Peiris, Janaka, Wicramasinghe, Ranjith, Fernando, Chamila, Abbas, Jessie, Withanage, Nethmini, Bandara, Makaranda, Mai, Duy Ton, Nguyen, Van Chi, Dao, Viet Phuong, Vuong, Xuan Trung, Nguyen, Tien Dung, Dinh, Trung Hieu, Phan, Ha Quan, Bui, Quoc Viet, Phung, Dinh Tho, Pham, Quang Tho, Pham, Dinh Dai, Do, Duc Thuan, Dang, Phuc Duc, Dang, Minh Duc, Nguyen, Dang Hai, Nguyen, Thi Phuong Nga, Nguyen, Quoc Huy, Pham, Quoc Dai, Chau, Quoc Vinh, Tai, Vinh Thy Van, Le, Tran Vinh, Le, Cong Tri, Tran, Ha Mai Khuong, Nguyen, Huu Khanh, Ngyen, Hoang Minh Thao, Vo, Duc Chien, Nguyen, Thai My Phuong, Tran, Trung Thanh, Vo, Thi Hanh Vi, Cao, Hao Nhien, Nguyen, Ba Thang, Le, Thi Ngoc Suong, La, Thien Duc, Pham, Chi Duc, Thai, Huy, Muñoz-Venturelli, Paula, de Silva, Asita, Thang, Nguyen Huy, Wahab, Kolawole W, Pandian, Jeyaraj D, Pontes-Neto, Octavio M, Shi, Haiping, Wang, Xia, Zhao, Yang, Li, Hao, Liu, Yi, Jiang, Yan, Wu, Bo, Liu, Ming, and Anderson, Craig S

Published
2023
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21. CT Texture-Based Nomogram in Ischemic Stroke to Differentiate Intracerebral Hemorrhage from Contrast Extravasation after Thrombectomy.

Author

An, Kun, Chen, Chun, Dong, Meijuan, Chen, Wei, and Cao, Yongjun

Subjects
HEMORRHAGIC stroke, STROKE patients, RECEIVER operating characteristic curves, ISCHEMIC stroke, CEREBRAL hemorrhage, EXTRAVASATION
Abstract

Introduction: Post-thrombectomy intraparenchymal hyperdensity (PTIH) in patients with acute ischemic stroke is a common CT sign, making it difficult for physicians to distinguish intracerebral hemorrhage in the early post-thrombectomy period. The aim of this study was to develop an effective model to differentiate intracerebral hemorrhage from contrast extravasation in patients with PTIH. Methods: We retrospectively collected information on patients who underwent endovascular thrombectomy at two stroke centers between August 2017 and January 2023. A total of 222 patients were included in the study, including 118 patients in the development cohort, 52 patients in the internal validation cohort, and 52 patients in the external validation cohort. The nomogram was constructed using R software based on independent predictors derived from the multivariate logistic regression analysis, including clinical factors and CT texture features extracted from hyperdense areas on CT images. The performance and accuracy of the derived nomogram were assessed by the area under the receiver operating characteristic curve (AUC-ROC) and calibration curves. Additionally, decision curve analysis was conducted to appraise the clinical utility of the nomogram. Results: Our nomogram was derived from two clinical factors (ASPECT score and onset to reperfusion time) and two CT texture features (variance and uniformity), with AUC-ROC of 0.943, 0.930, and 0.937 in the development, internal validation, and external validation cohorts, respectively. Furthermore, the calibration plot exhibited a strong agreement between the predicted outcome and the actual outcome. In addition, the decision curve analysis revealed the clinical utility of the nomogram in accurately predicting hemorrhage in patients with PTIH. Conclusion: The developed nomogram, based on clinical factors and CT texture features, proves to be effective in distinguishing intracerebral hemorrhage from contrast extravasation in patients with PTIH. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Global, regional, and national temporal trends of diet-related ischemic stroke mortality and disability from 1990 to 2019.

Author

Ge, Rongguang, You, Shoujiang, Zheng, Danni, Zhang, Zengli, Cao, Yongjun, and Chang, Jie

Abstract

Background: Stroke is the second leading cause of death and the third leading cause of disability in the general population worldwide. However, the changing trend of ischemic stroke burden attributable to various dietary risk factors has not been fully revealed and may contribute to a better understanding of stroke epidemiology. Aims: Our article aimed to evaluate the temporal trend of diet-related ischemic stroke burden to inform future research and policy-making. Methods: This analysis was based on the data from the Global Burden of Disease (GBD) Study 2019 (spanning years 1990 to 2019), and we used the joinpoint regression to model temporal trends in diet-related ischemic stroke burden across countries and regions of the world during the study period. Six specific dietary factors known to influence stroke risk, including sodium, red meat, fiber, vegetables, whole grains, and fruits, were evaluated in the GBD study to determine their individual and joint impact on ischemic stroke. The changing trend was primarily measured by the average annual percent change (AAPC). Age-standardized rates (ASRs) of mortality and years lived with disability (YLD) per 100,000 population were used to evaluate disease burden. Finally, the socioeconomic background, which was quantified as sociodemographic index (SDI), and its association with diet-related ischemic stroke burden were also explored with the Pearson correlation coefficient. Results: During the study period, the ischemic stroke ASR of mortality attributable to overall dietary risk decreased by an average of 1.6% per year, while the ASR of YLD decreased by an average of 0.2% per year. High sodium diet was still a key driver of diet-related ischemic stroke, accounting for 8.4% and 11.0% of deaths and disabilities, respectively, in 2019. In addition, we found a negative association between temporal evolution of stroke burden and socioeconomic background (r = −0.6603 for mortality and r = −0.4224 for disability, P < 0.001), which suggested that the developing countries with weak social and economic foundation faced greater challenges from the ongoing burden of diet-related strokes compared with developed countries. Conclusions: Our study found declining trends and revealed the current status of diet-related ischemic stroke mortality and disability. Interdisciplinary countermeasures involving the development of effective food policies, evidence-based guidelines, and public education are needed in the future to combat this global epidemic. Data access statement: The data used for analysis were open-access and can be obtained from https://vizhub.healthdata.org/gbd-results/. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Autophagy and Ischemic Stroke

Author

Zhang, Yanlin, Cao, Yongjun, Liu, Chunfeng, Crusio, Wim E., Series Editor, Dong, Haidong, Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, and Le, Weidong, editor

Published
2020
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33. Dioscin alleviates hashimoto’s thyroiditis by regulating the SUMOylation of IRF4 to promote CD4+CD25+Foxp3+ treg cell differentiation

Author

Cao Yongjun, Qiao Nan, Sun Yumeng, Jin Xiaowen, and Wen Weibo

Subjects
dioscorea nipponica makino, senp1, irf4, senp1 knockdown, Internal medicine, RC31-1245
Abstract

Dioscin has been used as a treatment for Hashimoto’s thyroiditis (HT) in China. However, the molecular mechanisms governing the modes of action of dioscin have not been elucidated. In this study, flow cytometry and Western blotting were used to identify the proportions of CD4+CD25+ regulatory T (Treg) cells and the expression of forkhead box P3 (Foxp3) and SUMO-specific protease 1 (SENP1) in HT patients’ peripheral blood mononuclear cells (PBMCs). A pTg-induced rat model of HT was established by injection of 100 μg pTg. Then, the model rats were randomly divided into three groups (n = 5): control (NC), model (HT) and dioscin treatment. After oral administration of dioscin each day for two weeks, CD4+CD25+Foxp3+ Treg cells were analysed by flow cytometry, and the protein expression levels of SENP1, Foxp3, SUMO-1 and SUMO-2/3 were measured by Western blotting. Co-immunoprecipitation (Co-IP) was used to identify the SUMOylation of interferon regulatory factor 4 (IRF4). The results showed that the proportions of CD4+CD25+ Treg cells and the expression of Foxp3 were significantly decreased in HT patients, but the expression of SENP1 was enhanced compared to healthy controls (HCs). However, compared to the pTg-induced HT rat group, the expression of Foxp3, SUMO-1, and SUMO-2/3 and the proportions of CD4+CD25+Foxp3+ Treg cells were increased, whereas the expression of SENP1 was decreased, in the dioscin-treated group. Furthermore, the SUMOylation of IRF4 was increased after SENP1 was knocked down. The results of our study indicate that dioscin can promote the differentiation of the CD4+CD25+Foxp3+ Treg cells and subsequently upregulate the SUMOylation of IRF4 by downregulating SENP1 expression.

Published
2021
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34. Single atom alloy clusters Agn−1X− (X = Cu, Au; n = 7–20) reacting with O2: Symmetry-adapted orbital model.

Author

Du, Qiuying, Huang, Lulu, Fu, Jiaqi, Cao, Yongjun, Xing, Xiaopeng, and Zhao, Jijun

Subjects
ELECTRON configuration, ATOMIC clusters, SINGLE electron transfer mechanisms, GOLD clusters, STRUCTURE-activity relationships
Abstract

Single atom alloy AgCu catalysts have attracted great attention, since doping the single Cu atom introduces narrow free-atom-like Cu 3d states in the electronic structure. These peculiar electronic states can reduce the activation energies in some reactions and offer valuable guidelines for improving catalytic performance. However, the geometric tuning effect of single Cu atoms in Ag catalysts and the structure–activity relationship of AgCu catalysts remain unclear. Here, we prepared well-resolved pristine Agn− as well as single atom alloy Agn−1Cu− and Agn−1Au− (n = 7–20) clusters and investigated their reactivity with O2. We found that replacing an Ag atom in Agn− (n = 15–18) with a Cu atom significantly increases the reactivity with O2, while replacement of an Ag with an Au atom has negligible effects. The adsorption of O2 on Agn− or Agn−1Cu− clusters follows the single electron transfer mechanism, in which the cluster activity is dependent on two descriptors, the energy level of α-HOMO (strong correlation) and the α-HOMO–LUMO gap (weak correlation). Our calculation demonstrated that the cluster arrangements caused by single Cu atom alloying would affect the above activity descriptors and, therefore, regulates clusters' chemical activity. In addition, the observed reactivity of clusters in the representative sizes with n = 17–19 can also be interpreted using the symmetry-adapted orbital model. Our work provides meaningful information to understand the chemical activities of related single-atom-alloy catalysts. [ABSTRACT FROM AUTHOR]

Published
2023
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35. A High Fibrinogen-to-Albumin Ratio on Admission is Associated with Early Neurological Deterioration Following Intravenous Thrombolysis in Patients with Acute Ischemic Stroke.

Author

Sun, Shifu, Cheng, Yongqing, Li, Lei, Zhu, Honghong, Liu, Changxia, and Cao, Yongjun

Subjects
STROKE patients, ISCHEMIC stroke, LOGISTIC regression analysis, CLINICAL deterioration, RECEIVER operating characteristic curves
Abstract

Purpose: The fibrinogen-to-albumin ratio (FAR) is a novel inflammation marker associated with various diseases. This study aimed to investigate the correlation between FAR and early neurological deterioration (END) after intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS). Patients and Methods: From September 1, 2021, to March 31, 2023, continuously recruited AIS patients who received IVT within 4.5 hours were included in the study. Blood samples were collected in the emergency room before IVT. The National Institutes of Health Stroke Scale (NIHSS) score was assessed upon admission and after thrombolysis within the first 24 hours. END was defined as an increase in the NIHSS score by ≥ 4 points within 24 hours after thrombolysis. Multivariate logistic regression analysis was conducted to explore the relationship between FAR and END, and a receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of FAR for END. Results: 343 participants were recruited, and 59 (17.2%) experienced END. Patients with END had higher FAR levels than those without END (P< 0.001). Multivariate logistic regression analysis showed that FAR was independently associated with END, both as a continuous variable and as a tertile variable (P< 0.005). After excluding patients with hemorrhagic transformation (HT), FAR remained independently associated with END (P< 0.005). The area under the curve (AUC) of FAR for predicting END was 0.650 (95% CI=0.571– 0.729, P< 0.001), with an optimal cutoff of 72.367 mg/g, a sensitivity of 61.6%, and a specificity of 62.6%. Conclusion: FAR upon admission was independently associated with END after IVT and can be an effective predictor. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Studies on Insertion/Deletion Residues for Functional Analysis and Improved Amination Activity in Meso -DAPDH from Corynebacterium glutamicum.

Author

Zhang, Yaning, Hao, Jiaying, Cao, Yongjun, Zhao, Wenjun, Liu, Hankun, Gao, Xiuzhen, and Ma, Qinyuan

Subjects
CORYNEBACTERIUM glutamicum, FUNCTIONAL analysis, AMINATION, PYRUVIC acid, PROTEIN engineering, DNA insertion elements
Abstract

Meso-diaminopimelate dehydrogenase (meso-DAPDH) from Corynebacterium glutamicum ATCC13032 (CgDAPDH) is a type I meso-DAPDH that shows obvious preference toward meso-diaminopimelate (meso-DAP) and exhibits almost no amination activity toward 2-keto acids. There are seven distinct conserved insertions and deletions (indels) between type I and type II meso-DAPDH. The current functional analysis of indels is not comprehensive in meso-DAPDH. Continuing from our previous work on these indels, we first examined the functions of the other indels shown as insertion residues in type I CgDAPDH. Alanine mutations in M216, T240, K289, and Q290 lost at least 40% of their activity, highlighting the importance of these four sites in CgDAPDH. Molecular dynamic analysis indicated that the four non-active sites altered the dynamic network of interactions within the protein. Subsequently, these four sites together with the previously identified indel-related residues R180, L176, and H193 were targeted by site-saturation mutagenesis to improve the amination ability of CgDAPDH toward pyruvic acid. The most significant improvement was observed with the mutant CgL176R, which showed a six-fold increase toward pyruvic acid in kcat/Km compared to wild-type CgDAPDH. Overall, our study provides new hotspots and ideas for the subsequent protein engineering of CgDAPDH, which may also be applied to other meso-DAPDHs. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Study on high voltage (5 V) spinel lithium manganese oxide LiNi0.5Mn1.5O4 by doping niobium.

Author

Li, Wei, Wang, Xiaotao, Wu, Dan, Kong, Dehao, Wu, Han, Mang, Lai, Liao, Bo, Tegus, O., Cao, Yongjun, and Haschuluu, Oimod

Subjects
LITHIUM manganese oxide, NIOBIUM, HIGH voltages, X-ray photoelectron spectroscopy, SPINEL, NIOBIUM compounds, MANGANESE oxides
Abstract

The effect of niobium ions with high‐valence doping on high‐voltage LiNi0.5Mn1.5O4 (LNMO) materials was investigated. LiNi0.5Mn1.5−xNbxO4 was prepared by doping high‐valent niobium ions into LiNi0.5Mn1.5O4 material using the organic assisted combustion method. The experimental samples were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X‐ray diffraction (XRD), X‐ray photoelectron spectroscopy (XPS), electrochemical impedance (EI), and cyclic voltammetry (CV) analysis. The experimental results show that ‐doping with high‐valence niobium ions changes the orientation of the crystal plane growth of spinel particles, and the morphology of these particles changes from the octahedral shape before doping to the spherical shape after doping. With the increase in doping amount, the crystal structure changes gradually, resulting in the Li0.96Nb1.01O3 impurity phase. The doping of high valence‐niobium ions increases the content of Mn3+ in the material, resulting in the appearance of a 4 V discharge platform and the formation of a 4.7 and 4 V discharge platforms. The doping of Nb can improve the cycling stability of LiNi0.5Mn1.5O4 material, but the specific capacity of the material is reduced. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Recent advances in treatment technology of wastewater containing H‐acid: a mini‐review.

Author

Cao, Yongjun, Hong, Qing, and Zhang, Yuanjian

Subjects
WASTEWATER treatment, CHEMICAL industry, TEXTILE industry, SEWAGE, POLLUTANTS
Abstract

H‐acid, a prevalent pollutant in dyeing wastewater, is primarily utilized in the pharmaceutical and textile industries. However, H‐acid treatment remains a challenge because of its high concentration, degradation resistance and poor biodegradability. This review examines the principles, characteristics, advantages and disadvantages of the most recently developed advanced practical H‐acid treatment technologies. Furthermore, this review analyzes the existing issues within these methods and explores potential future development trends. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2024
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