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3. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Published
2024
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6. Association between anger and suicidal ideation

Author

Cui, Ruifeng, Owsiany, Montgomery T., Turiano, Nicholas, and Fiske, Amy

Subjects
Research, Suicidal ideation -- Research, Anger -- Research, Psychological research
Abstract

Author(s): Ruifeng Cui [sup.1] , Montgomery T. Owsiany [sup.1] , Nicholas Turiano [sup.1] , Amy Fiske [sup.1] [sup.2] Author Affiliations: (1) grid.268154.c, 0000 0001 2156 6140, Department of Psychology, West [...], Suicide ranks among the 10 leading causes of death, with rates increasing among middle-aged adults. Anger is associated with suicide risk, but few studies have investigated this association in a primarily middle-aged sample. The present study investigated the association between state or trait anger and suicidal ideation among a national sample of 1255 middle-aged adults. Participants (M = 54.5, SD = 11.7; range = 34-84) were from the Midlife in the United States (MIDUS) Biomarker sub-project (2004-2009). In a multivariate model including age, sex, depression symptoms, angry temperament, and externalized anger, only angry temperament significantly predicted suicidal ideation independent of depression symptoms (OR = 1.13, p = .04). The associations between anger subscales and suicidal ideation were not moderated by gender or depression symptoms and were not mediated by interpersonal difficulties. Among middle-aged adults, angry temperament is associated with risk of suicidal ideation, irrespective of gender or depression symptoms. Assessing and addressing angry temperament in treatment may help decrease suicide risk.

Published
2023
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8. GhIMP10D, an inositol monophosphates family gene, enhances ascorbic acid and antioxidant enzyme activities to confer alkaline tolerance in Gossypium hirsutum L.

Author

Fan, Yapeng, Peng, Fanjia, Cui, Ruifeng, Wang, Shuai, Cui, Yupeng, Lu, Xuke, Huang, Hui, Ni, Kesong, Liu, Xiaoyu, Jiang, Tiantian, Feng, Xixian, Liu, Mengyue, Lei, Yuqian, Chen, Wenhua, Meng, Yuan, Han, Mingge, Wang, Delong, Yin, Zujun, Chen, Xiugui, Wang, Junjuan, Li, Yujun, Guo, Lixue, Zhao, Lanjie, and Ye, Wuwei

Published
2023
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11. Systematic analysis and expression of Gossypium 2ODD superfamily highlight the roles of GhLDOXs responding to alkali and other abiotic stress in cotton

Author

Jiang, Tiantian, Cui, Aihua, Cui, Yupeng, Cui, Ruifeng, Han, Mingge, Zhang, Yuexin, Fan, Yapeng, Huang, Hui, Feng, Xixian, Lei, Yuqian, Liu, Xiaoyu, Ni, Kesong, Zhang, Hong, Xu, Nan, Wang, Jing, Sun, Liangqing, Rui, Cun, Wang, Junjuan, Wang, Shuai, Chen, Xiugui, Lu, Xuke, Wang, Delong, Guo, Lixue, Zhao, Lanjie, Hao, Fushun, and Ye, Wuwei

Published
2023
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12. Combined transcriptomic and metabolomic analyses elucidate key salt-responsive biomarkers to regulate salt tolerance in cotton

Author

Han, Mingge, Cui, Ruifeng, Wang, Delong, Huang, Hui, Rui, Cun, Malik, Waqar Afzal, Wang, Jing, Zhang, Hong, Xu, Nan, Liu, Xiaoyu, Lei, Yuqian, Jiang, Tiantian, Sun, Liangqing, Ni, Kesong, Fan, Yapeng, Zhang, Yuexin, Wang, Junjuan, Chen, Xiugui, Lu, Xuke, Yin, Zujun, Wang, Shuai, Guo, Lixue, Zhao, Lanjie, Chen, Chao, and Ye, Wuwei

Published
2023
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14. Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans

Author

Bossarte, Robert M., Ross, Eric L., Liu, Howard, Turner, Brett, Bryant, Corey, Zainal, Nur Hani, Puac-Polanco, Victor, Ziobrowski, Hannah N., Cui, Ruifeng, Cipriani, Andrea, Furukawa, Toshiaki A., Leung, Lucinda B., Joormann, Jutta, Nierenberg, Andrew A., Oslin, David W., Pigeon, Wilfred R., Post, Edward P., Zaslavsky, Alan M., Zubizarreta, Jose R., Luedtke, Alex, Kennedy, Chris J., and Kessler, Ronald C.

Published
2023
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17. Age‐related Variation in Sleep‐dependent Obstruction in Surgically Naive Children.

Author

McArdle, Erica, Dewey, John, Cui, Ruifeng, Hunt, Connor, Kais, Amani, and Jaffal, Hussein

Abstract

Objective: Little is known about age‐related variations in sites and grade of sleep‐dependent airway obstruction in children with obstructive sleep apnea (OSA) or obstructive sleep‐disordered breathing (oSDB). The objective was to compare sites and grade of obstruction on drug‐induced sleep endoscopy (DISE) across different age groups of surgically naïve children with OSA or oSDB. Methods: A retrospective chart review was performed for surgically naïve children aged 0–18 years with OSA/oSDB who underwent DISE from July 2021 to August 2023. Participants were categorized into: infants (aged 0–1 years), younger toddlers (aged 1–2 years), older toddlers (aged 2–3 years), preschool (aged 3–5 years), younger school‐aged (aged 5–10 years), and older school‐aged (aged 10–18 years). On DISE, obstruction was rated 0 = none/mild, 1 = moderate, 2 = severe for inferior turbinates, adenoid, velum, palatine tonsils/lateral pharyngeal wall, lingual tonsils, tongue base, epiglottis, and supra‐arytenoid tissue. A series of multiple regression analyses were used to identify age differences in the grade of obstruction across all sites combined and at each individual site separately. Results: The sample consisted of 252 children aged 1 month to 17 years with 57.9% males. Older patients had greater total obstruction scores (B = 0.42, SE = 0.10, p < 0.01) and greater number of sites that were severely obstructed (B = 0.11, SE = 0.05, p = 0.03). Older age groups had more obstruction at inferior turbinates (p = 0.02), adenoid (p < 0.01), palatine tonsils/lateral pharyngeal wall (p < 0.01), lingual tonsil (p < 0.01), and base of tongue (p < 0.01). Younger age groups had more obstruction at the supra‐arytenoid tissue (p < 0.01). Conclusion: Varying patterns of sleep‐dependent airway obstruction should be expected across different age groups in children with OSA or oSDB. Level of Evidence: 3 Laryngoscope, 135:463–468, 2025 [ABSTRACT FROM AUTHOR]

Published
2025
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20. Rapid Production Nasal Osteotomy Simulators With Multi‐Modality Manufacturing: 3D Printing, Casting, and Molding.

Author

Tumlin, Parker, Sunyecz, Ian, Cui, Ruifeng, Armeni, Mark, and Freiser, Monika E.

Abstract

Objective: To expand and improve upon previously described nasal osteotomy models with the goals of decreasing cost and production time while ensuring model fidelity. To assess change in participant confidence in their understanding of and ability to perform nasal osteotomies following completion of the simulation course. Study Design: Prospective study. Setting: Simulation training course for otolaryngology residents at West Virginia University. Methods: A combined methodology of 3D printing, silicone molding, and resin casting was used to design a nasal osteotomy model to address material issues such as print delamination. Multiple models were then used in a simulation lab on performing nasal osteotomies. Model utility and impact on participant confidence was assessed at baseline, postlecture, and postsimulation lab. Results: Using a combined manufacturing methodology, we achieved a production time reduction of 97.71% and a cost reduction of 82.02% for this polyurethane resin nasal osteotomy model relative to a previously described osteotomy model. Participants in the simulation course were noted to have a significant improvement in confidence in their understanding of and ability to perform nasal osteotomies from baseline and postlecture and also from postlecture and postsimulation lab (P <.05 for all). Conclusion: By incorporating multiple manufacturing modalities (molding and casting) in addition to 3D printing, this study achieved a large reduction in both production time and cost in fabrication of a nasal osteotomy simulator and addressed material limitations imposed by fused deposition modeling printers. This design methodology serves as an example on how these barriers may be addressed in unrelated simulation projects. Model fidelity was improved with addition of a silicone soft tissue midface. Improvement in participant confidence was noted following completion of the simulation lab. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Integrated Cognitive Behavioral Therapy Versus Cognitive Processing Therapy for Adults With Depression, Substance Use Disorder, and Trauma.

Author

Haller, Moira, Norman, Sonya B, Cummins, Kevin, Trim, Ryan S, Xu, Xiaomin, Cui, Ruifeng, Allard, Carolyn B, Brown, Sandra A, and Tate, Susan R

Subjects
Humans, Substance-Related Disorders, Diagnosis, Dual (Psychiatry), Depression, Stress Disorders, Post-Traumatic, Adult, Middle Aged, Veterans, Female, Male, Cognitive Behavioral Therapy, Cognitive processing therapy, Integrated treatment, PTSD, Substance use disorder, Diagnosis, Dual, Stress Disorders, Post-Traumatic, Cognitive Therapy, Substance Abuse, Public Health and Health Services, Psychology
Abstract

The comorbidity of substance use disorder (SUD), depression, and PTSD is common among veterans. Prior research has shown that among veterans with SUD and depression, those with PTSD did not maintain cognitive-behavioral treatment gains as well as those without PTSD. Thus, the current study was designed to evaluate whether adding trauma-focused treatment following an initial group-based integrated cognitive behavioral treatment (ICBT) for SUD and depression improved treatment outcomes. Participants were 123 veterans (89% male) recruited from the VA San Diego Healthcare System. All participants received ICBT in twice weekly, group-delivered sessions for 12 weeks (Phase 1). Participants were then randomized to receive 12 sessions of individual follow-up sessions (Phase 2) utilizing either ICBT or cognitive processing therapy that was modified to integrate SUD treatment (CPT-M). Results indicated that PTSD and depression symptoms slightly improved at the end of Phase 1 group ICBT and further improved through Phase 2 individual treatment (except for participants without PTSD who received CPT-M), with treatment gains maintained one year later. Substance use significantly improved at the end of Phase 1 group ICBT and these improvements were maintained through Phase 2 and the one year follow-up. Participants in the trauma-focused Phase 2 treatment (CPT-M) exhibited similar levels of symptom reduction and maintenance of treatment gains as those in the non-trauma-focused Phase 2 treatment (ICBT). However, there was a slight advantage for Phase 2 CPT-M over Phase 2 ICBT with respect to heavy drinking outcomes for individuals with PTSD. Overall, the combination of group ICBT followed by either CPT-M or ICBT individual therapy appears to be effective for veterans with depression, SUD, and trauma history.

Published
2016

25. Chronic Physical Health Problems Moderate Changes in Depression and Substance Use Among Dual Diagnosed Individuals During and After Treatment

Author

Cui, Ruifeng, Tate, Susan R, Cummins, Kevin, Skidmore, Jessica R, and Brown, Sandra A

Subjects
Clinical and Health Psychology, Health Services and Systems, Health Sciences, Psychology, Substance Misuse, Depression, Mental Health, Mental Illness, Brain Disorders, 6.6 Psychological and behavioural, Mental health, Good Health and Well Being, Adult, Cognitive Behavioral Therapy, Depressive Disorder, Diagnosis, Dual (Psychiatry), Female, Health Status, Humans, Male, Middle Aged, Psychotherapy, Substance-Related Disorders, Treatment Outcome, physical health, depression, substance use disorder, treatment, Public Health and Health Services, Substance Abuse, Public health, Applied and developmental psychology, Clinical and health psychology
Abstract

BackgroundPhysical illnesses frequently co-occur with depression and substance use disorders and may impact their improvement. Physical illness symptoms may overlap with or exacerbate somatic symptoms of depression. Individuals may use substances to cope with symptoms of physical illness.ObjectivesWe examined whether chronic physical health problems moderated changes in depression and substance use among dual diagnosed individuals during and in the year following treatment.MethodsParticipants were recruited from a Veterans Affairs dual diagnosis outpatient program between March 2000 and November 2007 and were randomized to either Integrated Cognitive-Behavioral Therapy or Twelve-Step Facilitation Therapy. A total of 214 veterans with assessment data for the variables of interest were included in analyses. Participants completed quarterly depression, substance use, and health assessments over an 18 month period. We used linear-mixed effects models to analyze patterns of change for depression and substance use.ResultsIndividuals with severe chronic health problems and higher intake depression showed slower improvements in both nonsomatic and somatic depression symptoms. Individuals with severe chronic health problems and higher midtreatment substance use showed less improvement in substance use.ConclusionsAssessing and addressing physical health issues during depression and substance use disorder treatment may improve outcomes.

Published
2015

30. Raffinose degradation-related gene GhAGAL3 was screened out responding to salinity stress through expression patterns of GhAGALs family genes

Author

Chen, Wenhua, primary, Cui, Yupeng, additional, He, Yunxin, additional, Zhao, Lanjie, additional, Cui, Ruifeng, additional, Liu, Xiaoyu, additional, Huang, Hui, additional, Zhang, Yuexin, additional, Fan, Yapeng, additional, Feng, Xixian, additional, Ni, Kesong, additional, Jiang, Tiantian, additional, Han, Mingge, additional, Lei, Yuqian, additional, Liu, Mengyue, additional, Meng, Yuan, additional, Chen, Xiugui, additional, Lu, Xuke, additional, Wang, Delong, additional, Wang, Junjuan, additional, Wang, Shuai, additional, Guo, Lixue, additional, Chen, Quanjia, additional, and Ye, Wuwei, additional

Published
2023
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32. GhLCYε-3 characterized as a lycopene cyclase gene responding to drought stress in cotton

Author

Ni, Kesong, primary, Lu, Xuke, additional, Li, Shuyan, additional, Li, Fei, additional, Zhang, Yuexin, additional, Cui, Ruifeng, additional, Fan, Yapeng, additional, Huang, Hui, additional, Chen, Xiugui, additional, Wang, Junjuan, additional, Wang, Shuai, additional, Guo, Lixue, additional, Zhao, Lanjie, additional, He, Yunxin, additional, and Ye, Wuwei, additional

Published
2023
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33. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Liu, Jiamin, Li, Yan, Ge, Jinzhuo, Yan, Xiaofang, Zhang, Haibo, Zheng, Xin, Lu, Jiapeng, Li, Xi, Gao, Yan, Lei, Lubi, Liu, Jing, Li, Jing, Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Lin, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yan, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Abstract

Uncertainty exists about whether lowering systolic blood pressure to less than 120 mm Hg is superior to that of less than 140 mm Hg, particularly in patients with diabetes and patients with previous stroke.

Published
2024
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34. Age Moderates the Relation between Sleep Problems and Suicide Risk.

Author

Bolstad, Courtney J., Cui, Ruifeng, Fiske, Amy, and Nadorff, Michael R.

Subjects
*SUICIDE risk factors, *MENTAL depression risk factors, *RISK assessment, *CROSS-sectional method, *EMPLOYEES, *QUESTIONNAIRES, *SEX distribution, *AGE distribution, *DESCRIPTIVE statistics, *ANALYSIS of variance, *SLEEP disorders, *REGRESSION analysis, *DISEASE complications, *OLD age, *ADULTS
Abstract

This cross-sectional study examined whether age moderates the relation between sleep problems and suicide risk and investigated whether sleep problems are differentially associated with suicide risk in younger (18–40) and older (60+) adults. MTurk workers (N = 733) completed the Pittsburgh Sleep Quality Index, Suicidal Behavior Questionnaire–Revised, Patient Health Questionnaire, and demographic questions. Analysis of variance and linear regressions were utilized. Older adults scored lower on four PSQI components, symptoms of depression, and suicide risk than younger adults. Age significantly moderated the relation between sleep problems and suicide risk after controlling for gender and depressive symptoms, F(5, 635) = 72.38, p <.001. Sleep problems significantly related to suicide risk in younger adults (t = 6.47, p <.001) but not in older adults (t = 0.57, p =.57). Sleep medication use was related to suicide risk in both groups, whereas daytime dysfunction was related to suicide risk in older adults and sleep disturbances were related to suicide risk in younger adults. The relation between sleep problems and suicide risk differs between younger and older adults. This study adds to the literature suggesting that sleep medications may not be appropriate for older adults. Sleep problems are significantly related to suicide risk in younger adults but not older adults. Sleep medication use is associated with suicide risk regardless of age. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Characterization and gene expression patterns analysis implies BSK family genes respond to salinity stress in cotton

Author

Lei, Yuqian, primary, Cui, Yupeng, additional, Cui, Ruifeng, additional, Chen, Xiugui, additional, Wang, Junjuan, additional, Lu, Xuke, additional, Wang, Delong, additional, Wang, Shuai, additional, Guo, Lixue, additional, Zhang, Yuexin, additional, Rui, Cun, additional, Fan, Yapeng, additional, Han, Mingge, additional, Zhao, Lanjie, additional, Zhang, Hong, additional, Liu, Xiaoyu, additional, Xu, Nan, additional, Wang, Jing, additional, Huang, Hui, additional, Feng, Xixian, additional, Xi, Yanlong, additional, Ni, Kesong, additional, Zhang, Menghao, additional, Jiang, Tiantian, additional, and Ye, Wuwei, additional

Published
2023
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42. Additional file 1 of Combined transcriptomic and metabolomic analyses elucidate key salt-responsive biomarkers to regulate salt tolerance in cotton

Author

Han, Mingge, Cui, Ruifeng, Wang, Delong, Huang, Hui, Rui, Cun, Malik, Waqar Afzal, Wang, Jing, Zhang, Hong, Xu, Nan, Liu, Xiaoyu, Lei, Yuqian, Jiang, Tiantian, Sun, Liangqing, Ni, Kesong, Fan, Yapeng, Zhang, Yuexin, Wang, Junjuan, Chen, Xiugui, Lu, Xuke, Yin, Zujun, Wang, Shuai, Guo, Lixue, Zhao, Lanjie, Chen, Chao, and Ye, Wuwei

Abstract

Additional file 1.

Published
2023
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46. Cognitive Behavioral Therapy (CBT) Telehealth Augmented With a CBT Smartphone Application to Address Type 2 Diabetes Self-Management: A Randomized Pilot Trial

Author

Callan, Judith A., primary, Sereika, Susan M., additional, Cui, Ruifeng, additional, Tamres, Lisa K., additional, Tarneja, Monisha, additional, Greene, Brian, additional, Van Slyke, Ashley, additional, Wu, Megan, additional, Lukac, Gabriella R., additional, and Dunbar-Jacob, Jacqueline, additional

Published
2022
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49. Analysis of the histidine kinase gene family and the role of GhHK8 in response to drought tolerance in cotton.

Author

Zhao, Lanjie, Wang, Yongbo, Cui, Ruifeng, Cui, Yupeng, Lu, Xuke, Chen, Xiugui, Wang, Junjuan, Wang, Delong, Yin, Zujun, Wang, Shuai, Peng, Fanjia, Guo, Lixue, Chen, Chao, and Ye, Wuwei

Abstract

As an important member of the two‐component system (TCS), histidine kinases (HKs) play important roles in various plant developmental processes and signal transduction in response to a wide range of biotic and abiotic stresses. So far, the HK gene family has not been investigated in Gossypium. In this study, a total of 177 HK gene family members were identified in cotton. They were further divided into seven groups, and the protein characteristics, genetic relationship, gene structure, chromosome location, collinearity, and cis‐elements identification were comprehensively analyzed. Whole genome duplication (WGD) / segmental duplication may be the reason why the number of HK genes doubled in tetraploid Gossypium species. Expression analysis revealed that most cotton HK genes were mainly expressed in the reproductive organs and the fiber at initial stage. Gene expression analysis revealed that HK family genes are involved in cotton abiotic stress, especially drought stress and salt stress. In addition, gene interaction networks showed that HKs were involved in the regulation of cotton abiotic stress, especially drought stress. VIGS experiments have shown that GhHK8 is a negative regulatory factor in response to drought stress. Our systematic analysis provided insights into the characteristics of the HK genes in cotton and laid a foundation for further exploring their potential in drought stress resistance in cotton. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Development of a model to predict antidepressant treatment response for depression among Veterans.

Author

Puac-Polanco, Victor, Ziobrowski, Hannah N., Ross, Eric L., Liu, Howard, Turner, Brett, Cui, Ruifeng, Leung, Lucinda B., Bossarte, Robert M., Bryant, Corey, Joormann, Jutta, Nierenberg, Andrew A., Oslin, David W., Pigeon, Wilfred R., Post, Edward P., Zainal, Nur Hani, Zaslavsky, Alan M., Zubizarreta, Jose R., Luedtke, Alex, Kennedy, Chris J., and Cipriani, Andrea

Subjects
ANTIDEPRESSANTS, CLINICAL decision support systems, SELF-evaluation, MACHINE learning, TREATMENT effectiveness, PSYCHOLOGICAL tests, MENTAL depression, DESCRIPTIVE statistics, RESEARCH funding, VETERANS, PREDICTION models, RECEIVER operating characteristic curves, PROBABILITY theory, COMORBIDITY, PSYCHOLOGICAL resilience
Abstract

Background: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). Methods: A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. Results: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. Conclusions: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection. [ABSTRACT FROM AUTHOR]

Published
2023
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