👤 Tan-Huan Chen

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2981
Articles
1996
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Also published as: Ai-Qun Chen, Aiping Chen, Alex Chen, Alex F Chen, Alice P Chen, Alice Y Chen, Alice Ye A Chen, Allen Menglin Chen, Alon Chen, Alvin Chen, An Chen, Andrew Chen, Anqi Chen, Aoshuang Chen, Aozhou Chen, B Chen, B-S Chen, Baihua Chen, Ban Chen, Bang Chen, Bang-dang Chen, Bao-Bao Chen, Bao-Fu Chen, Bao-Sheng Chen, Bao-Ying Chen, Baofeng Chen, Baojiu Chen, Baolin Chen, Baosheng Chen, Baoxiang Chen, Beidong Chen, Beijian Chen, Ben-Kuen Chen, Benjamin Chen, Benjamin Jieming Chen, Benjamin P C Chen, Beth L Chen, Bihong T Chen, Bin Chen, Bing Chen, Bing-Bing Chen, Bing-Feng Chen, Bing-Huei Chen, Bingdi Chen, Bingqian Chen, Bingqing Chen, Bingyu Chen, Binlong Chen, Binzhen Chen, Bo Chen, Bo-Fang Chen, Bo-Jun Chen, Bo-Rui Chen, Bo-Sheng Chen, Bohe Chen, Bohong Chen, Bosong Chen, Bowang Chen, Bowei Chen, Bowen Chen, Boyu Chen, Brian Chen, C Chen, C Y Chen, C Z Chen, C-Y Chen, Cai-Long Chen, Caihong Chen, Can Chen, Cancan Chen, Canrong Chen, Canyu Chen, Caressa Chen, Carl Pc Chen, Carol Chen, Carol X-Q Chen, Catherine Qing Chen, Ceshi Chen, Chan Chen, Chang Chen, Chang-Lan Chen, Chang-Zheng Chen, Changjie Chen, Changya Chen, Changyan Chen, Chanjuan Chen, Chao Chen, Chao-Jung Chen, Chao-Wei Chen, Chaochao Chen, Chaojin Chen, Chaoli Chen, Chaoping Chen, Chaoqun Chen, Chaoran Chen, Chaoyi Chen, Chaoyue Chen, Chen Chen, Chen-Mei Chen, Chen-Sheng Chen, Chen-Yu Chen, Cheng Chen, Cheng-Fong Chen, Cheng-Sheng Chen, Cheng-Yi Chen, Cheng-Yu Chen, Chengchuan Chen, Chengchun Chen, Chengde Chen, Chengsheng Chen, Chengwei Chen, Chenyang Chen, Chi Chen, Chi-Chien Chen, Chi-Hua Chen, Chi-Long Chen, Chi-Yu Chen, Chi-Yuan Chen, Chi-Yun Chen, Chian-Feng Chen, Chider Chen, Chien-Hsiun Chen, Chien-Jen Chen, Chien-Lun Chen, Chien-Ting Chen, Chien-Yu Chen, Chih-Chieh Chen, Chih-Mei Chen, Chih-Ping Chen, Chih-Ta Chen, Chih-Wei Chen, Chih-Yi Chen, Chin-Chuan Chen, Ching Kit Chen, Ching-Hsuan Chen, Ching-Jung Chen, Ching-Wen Chen, Ching-Yi Chen, Ching-Yu Chen, Chiqi Chen, Chiung Mei Chen, Chiung-Mei Chen, Chixiang Chen, Chong Chen, Chongyang Chen, Christina Y Chen, Christina Yingxian Chen, Christopher S Chen, Chu Chen, Chu-Huang Chen, Chuanbing Chen, Chuannan Chen, Chuanzhi Chen, Chuck T Chen, Chueh-Tan Chen, Chujie Chen, Chun Chen, Chun-An Chen, Chun-Chi Chen, Chun-Fa Chen, Chun-Han Chen, Chun-Houh Chen, Chun-Wei Chen, Chun-Yuan Chen, Chung-Hao Chen, Chung-Hsing Chen, Chung-Hung Chen, Chung-Jen Chen, Chung-Yung Chen, Chunhai Chen, Chunhua Chen, Chunji Chen, Chunjie Chen, Chunlin Chen, Chunnuan Chen, Chunxiu Chen, Chuo Chen, Chuyu Chen, Cindi Chen, Constance Chen, Cuicui Chen, Cuie Chen, Cuilan Chen, Cuimin Chen, Cuncun Chen, D F Chen, D M Chen, D-F Chen, D. Chen, Dafang Chen, Daijie Chen, Daiwen Chen, Daiyu Chen, Dake Chen, Dali Chen, Dan Chen, Dan-Dan Chen, Dandan Chen, Danlei Chen, Danli Chen, Danmei Chen, Danna Chen, Danni Chen, Danxia Chen, Danxiang Chen, Danyang Chen, Danyu Chen, Daoyuan Chen, Dapeng Chen, Dawei Chen, Defang Chen, Dejuan Chen, Delong Chen, Denghui Chen, Dengpeng Chen, Deqian Chen, Dexi Chen, Dexiang Chen, Dexiong Chen, Deying Chen, Deyu Chen, Di Chen, Di-Long Chen, Dian Chen, Dianke Chen, Ding Chen, Diyun Chen, Dong Chen, Dong-Mei Chen, Dong-Yi Chen, Dongli Chen, Donglong Chen, Dongquan Chen, Dongrong Chen, Dongsheng Chen, Dongxue Chen, Dongyan Chen, Dongyin Chen, Du-Qun Chen, Duan-Yu Chen, Duo Chen, Duo-Xue Chen, Duoting Chen, E S Chen, Eleanor Y Chen, Elizabeth H Chen, Elizabeth S Chen, Elizabeth Suchi Chen, Emily Chen, En-Qiang Chen, Erbao Chen, Erfei Chen, Erqu Chen, Erzhen Chen, Everett H Chen, F Chen, F-K Chen, Fa Chen, Fa-Xi Chen, Fahui Chen, Fan Chen, Fang Chen, Fang-Pei Chen, Fang-Yu Chen, Fang-Zhi Chen, Fang-Zhou Chen, Fangfang Chen, Fangli Chen, Fangyan Chen, Fangyuan Chen, Faye H Chen, Fei Chen, Fei Xavier Chen, Feifan Chen, Feifeng Chen, Feilong Chen, Feixue Chen, Feiyang Chen, Feiyu Chen, Feiyue Chen, Feng Chen, Feng-Jung Chen, Feng-Ling Chen, Fenghua Chen, Fengju Chen, Fengling Chen, Fengming Chen, Fengrong Chen, Fengwu Chen, Fengyang Chen, Fred K Chen, Fu Chen, Fu-Shou Chen, Fumei Chen, Fusheng Chen, Fuxiang Chen, Gang Chen, Gao B Chen, Gao Chen, Gao-Feng Chen, Gaoyang Chen, Gaoyu Chen, Gaozhi Chen, Gary Chen, Gary K Chen, Ge Chen, Gen-Der Chen, Geng Chen, Gengsheng Chen, Ginny I Chen, Gong Chen, Gongbo Chen, Gonghai Chen, Gonglie Chen, Guan-Wei Chen, Guang Chen, Guang-Chao Chen, Guang-Yu Chen, Guangchun Chen, Guanghao Chen, Guanghong Chen, Guangjie Chen, Guangju Chen, Guangliang Chen, Guanglong Chen, Guangnan Chen, Guangping Chen, Guangquan Chen, Guangyao Chen, Guangyi Chen, Guangyong Chen, Guanjie Chen, Guanren Chen, Guanyu Chen, Guanzheng Chen, Gui Mei Chen, Gui-Hai Chen, Gui-Lai Chen, Guihao Chen, Guiqian Chen, Guiquan Chen, Guiying Chen, Guo Chen, Guo-Chong Chen, Guo-Jun Chen, Guo-Rong Chen, Guo-qing Chen, Guochao Chen, Guochong Chen, Guofang Chen, Guohong Chen, Guohua Chen, Guojun Chen, Guoliang Chen, Guopu Chen, Guoshun Chen, Guoxun Chen, Guozhong Chen, Guozhou Chen, H Chen, H Q Chen, H T Chen, Hai-Ning Chen, Haibing Chen, Haibo Chen, Haide Chen, Haifeng Chen, Haijiao Chen, Haimin Chen, Haiming Chen, Haining Chen, Haiqin Chen, Haiquan Chen, Haitao Chen, Haiyan Chen, Haiyang Chen, Haiyi Chen, Haiying Chen, Haiyu Chen, Haiyun Chen, Han Chen, Han-Bin Chen, Han-Chun Chen, Han-Hsiang Chen, Han-Min Chen, Hanbei Chen, Hang Chen, Hangang Chen, Hanjing Chen, Hanlin Chen, Hanqing Chen, Hanwen Chen, Hanxi Chen, Hanyong Chen, Hao Chen, Hao Yu Chen, Hao-Zhu Chen, Haobo Chen, Haodong Chen, Haojie Chen, Haoran Chen, Haotai Chen, Haotian Chen, Haoting Chen, Haoyun Chen, Haozhu Chen, Harn-Shen Chen, Haw-Wen Chen, He-Ping Chen, Hebing Chen, Hegang Chen, Hehe Chen, Hekai Chen, Heng Chen, Heng-Sheng Chen, Heng-Yu Chen, Hengsan Chen, Hengsheng Chen, Hengyu Chen, Heni Chen, Herbert Chen, Hetian Chen, Heye Chen, Hong Chen, Hong Yang Chen, Hong-Sheng Chen, Hongbin Chen, Hongbo Chen, Hongen Chen, Honghai Chen, Honghui Chen, Honglei Chen, Hongli Chen, Hongmei Chen, Hongmin Chen, Hongmou Chen, Hongqi Chen, Hongqiao Chen, Hongshan Chen, Hongxiang Chen, Hongxing Chen, Hongxu Chen, Hongyan Chen, Hongyu Chen, Hongyue Chen, Hongzhi Chen, Hou-Tsung Chen, Hou-Zao Chen, Hsi-Hsien Chen, Hsiang-Wen Chen, Hsiao-Jou Cortina Chen, Hsiao-Tan Chen, Hsiao-Wang Chen, Hsiao-Yun Chen, Hsin-Han Chen, Hsin-Hong Chen, Hsin-Hung Chen, Hsin-Yi Chen, Hsiu-Wen Chen, Hsuan-Yu Chen, Hsueh-Fen Chen, Hu Chen, Hua Chen, Hua-Pu Chen, Huachen Chen, Huafei Chen, Huaiyong Chen, Hualan Chen, Huali Chen, Hualin Chen, Huan Chen, Huan-Xin Chen, Huanchun Chen, Huang Chen, Huang-Pin Chen, Huangtao Chen, Huanhua Chen, Huanhuan Chen, Huanxiong Chen, Huaping Chen, Huapu Chen, Huaqiu Chen, Huatao Chen, Huaxin Chen, Huayu Chen, Huei-Rong Chen, Huei-Yan Chen, Huey-Miin Chen, Hui Chen, Hui Mei Chen, Hui-Chun Chen, Hui-Fen Chen, Hui-Jye Chen, Hui-Ru Chen, Hui-Wen Chen, Hui-Xiong Chen, Hui-Zhao Chen, Huichao Chen, Huijia Chen, Huijiao Chen, Huijie Chen, Huimei Chen, Huimin Chen, Huiqin Chen, Huiqun Chen, Huiru Chen, Huishan Chen, Huixi Chen, Huixian Chen, Huizhi Chen, Hung-Chang Chen, Hung-Chi Chen, Hung-Chun Chen, Hung-Po Chen, Hung-Sheng Chen, I-Chun Chen, I-M Chen, Ida Y-D Chen, Irwin Chen, Ivy Xiaoying Chen, J Chen, Jacinda Chen, Jack Chen, Jake Y Chen, Jason A Chen, Jeanne Chen, Jen-Hau Chen, Jen-Sue Chen, Jennifer F Chen, Jenny Chen, Jeremy J W Chen, Ji-ling Chen, Jia Chen, Jia Min Chen, Jia Wei Chen, Jia-De Chen, Jia-Feng Chen, Jia-Lin Chen, Jia-Mei Chen, Jia-Shun Chen, Jiabing Chen, Jiacai Chen, Jiacheng Chen, Jiade Chen, Jiahao Chen, Jiahua Chen, Jiahui Chen, Jiajia Chen, Jiajing Chen, Jiajun Chen, Jiakang Chen, Jiale Chen, Jiali Chen, Jialing Chen, Jiamiao Chen, Jiamin Chen, Jian Chen, Jian-Guo Chen, Jian-Hua Chen, Jian-Jun Chen, Jian-Kang Chen, Jian-Min Chen, Jian-Qiao Chen, Jian-Qing Chen, Jianan Chen, Jianfei Chen, Jiang Chen, Jiang Ye Chen, Jiang-hua Chen, Jianghua Chen, Jiangxia Chen, Jianhua Chen, Jianhui Chen, Jiani Chen, Jianjun Chen, Jiankui Chen, Jianlin Chen, Jianmin Chen, Jianping Chen, Jianshan Chen, Jiansu Chen, Jianxiong Chen, Jianzhong Chen, Jianzhou Chen, Jiao Chen, Jiao-Jiao Chen, Jiaohua Chen, Jiaping Chen, Jiaqi Chen, Jiaqing Chen, Jiaren Chen, Jiarou Chen, Jiawei Chen, Jiawen Chen, Jiaxin Chen, Jiaxu Chen, Jiaxuan Chen, Jiayao Chen, Jiaye Chen, Jiayi Chen, Jiayuan Chen, Jichong Chen, Jie Chen, Jie-Hua Chen, Jiejian Chen, Jiemei Chen, Jien-Jiun Chen, Jihai Chen, Jijun Chen, Jimei Chen, Jin Chen, Jin-An Chen, Jin-Ran Chen, Jin-Shuen Chen, Jin-Wu Chen, Jin-Xia Chen, Jina Chen, Jinbo Chen, Jindong Chen, Jing Chen, Jing-Hsien Chen, Jing-Wen Chen, Jing-Xian Chen, Jing-Yuan Chen, Jing-Zhou Chen, Jingde Chen, Jinghua Chen, Jingjing Chen, Jingli Chen, Jinglin Chen, Jingming Chen, Jingnan Chen, Jingqing Chen, Jingshen Chen, Jingteng Chen, Jinguo Chen, Jingxuan Chen, Jingyao Chen, Jingyi Chen, Jingyuan Chen, Jingzhao Chen, Jingzhou Chen, Jinhao Chen, Jinhuang Chen, Jinli Chen, Jinlun Chen, Jinquan Chen, Jinsong Chen, Jintian Chen, Jinxuan Chen, Jinyan Chen, Jinyong Chen, Jion Chen, Jiong Chen, Jiongyu Chen, Jishun Chen, Jiu-Chiuan Chen, Jiujiu Chen, Jiwei Chen, Jiyan Chen, Jiyuan Chen, Jonathan Chen, Joy J Chen, Juan Chen, Juan-Juan Chen, Juanjuan Chen, Juei-Suei Chen, Juhai Chen, Jui-Chang Chen, Jui-Yu Chen, Jun Chen, Jun-Long Chen, Junchen Chen, Junfei Chen, Jung-Sheng Chen, Junhong Chen, Junhui Chen, Junjie Chen, Junling Chen, Junmin Chen, Junming Chen, Junpan Chen, Junpeng Chen, Junqi Chen, Junqin Chen, Junsheng Chen, Junshi Chen, Junyang Chen, Junyi Chen, Junyu Chen, K C Chen, Kai Chen, Kai-En Chen, Kai-Ming Chen, Kai-Ting Chen, Kai-Yang Chen, Kaifu Chen, Kaijian Chen, Kailang Chen, Kaili Chen, Kaina Chen, Kaiquan Chen, Kan Chen, Kang Chen, Kang-Hua Chen, Kangyong Chen, Kangzhen Chen, Katharine Y Chen, Katherine C Chen, Ke Chen, Kecai Chen, Kehua Chen, Kehui Chen, Kelin Chen, Ken Chen, Kenneth L Chen, Keping Chen, Kequan Chen, Kevin Chen, Kewei Chen, Kexin Chen, Keyan Chen, Keyang Chen, Keying Chen, Keyu Chen, Keyuan Chen, Kuan-Jen Chen, Kuan-Ling Chen, Kuan-Ting Chen, Kuan-Yu Chen, Kuangyang Chen, Kuey Chu Chen, Kui Chen, Kun Chen, Kun-Chieh Chen, Kunmei Chen, Kunpeng Chen, L B Chen, L F Chen, Lan Chen, Lang Chen, Lankai Chen, Lanlan Chen, Lanmei Chen, Le Chen, Le Qi Chen, Lei Chen, Lei-Chin Chen, Lei-Lei Chen, Leijie Chen, Lena W Chen, Leqi Chen, Letian Chen, Lexia Chen, Li Chen, Li Jia Chen, Li-Chieh Chen, Li-Hsien Chen, Li-Hsin Chen, Li-Hua Chen, Li-Jhen Chen, Li-Juan Chen, Li-Mien Chen, Li-Nan Chen, Li-Tzong Chen, Li-Zhen Chen, Li-hong Chen, Lian Chen, Lianfeng Chen, Liang Chen, Liang-Kung Chen, Liangkai Chen, Liangsheng Chen, Liangwan Chen, Lianmin Chen, Liaobin Chen, Lichang Chen, Lichun Chen, Lidian Chen, Lie Chen, Liechun Chen, Lifang Chen, Lifen Chen, Lifeng Chen, Ligang Chen, Lihong Chen, Lihua Chen, Lijin Chen, Lijuan Chen, Lili Chen, Limei Chen, Limin Chen, Liming Chen, Lin Chen, Lina Chen, Linbo Chen, Ling Chen, Ling-Yan Chen, Lingfeng Chen, Lingjun Chen, Lingli Chen, Lingxia Chen, Lingxue Chen, Lingyi Chen, Linjie Chen, Linlin Chen, Linna Chen, Linxi Chen, Linyi Chen, Liping Chen, Liqiang Chen, Liugui Chen, Liujun Chen, Liutao Chen, Lixia Chen, Lixian Chen, Liyun Chen, Lizhen Chen, Lizhu Chen, Lo-Yun Chen, Long Chen, Long-Jiang Chen, Longqing Chen, Longyun Chen, Lu Chen, Lu Hua Chen, Lu-Biao Chen, Lu-Zhu Chen, Lulu Chen, Luming Chen, Luyi Chen, Luzhu Chen, M Chen, M L Chen, Man Chen, Man-Hua Chen, Mao Chen, Mao-Yuan Chen, Maochong Chen, Maorong Chen, Marcus Y Chen, Mark I-Cheng Chen, Max Jl Chen, Mechi Chen, Mei Chen, Mei-Chi Chen, Mei-Chih Chen, Mei-Hsiu Chen, Mei-Hua Chen, Mei-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, Siteng Chen, Siting Chen, Siyi Chen, Siyu Chen, Siyu S Chen, Siyuan Chen, Siyue Chen, Size Chen, Song Chen, Song-Mei Chen, Songfeng Chen, Suet N Chen, Suet Nee Chen, Sufang Chen, Suipeng Chen, Sulian Chen, Suming Chen, Sun Chen, Sung-Fang Chen, Suning Chen, Sunny Chen, Sy-Jou Chen, Syue-Ting Chen, Szu-Chi Chen, Szu-Chia Chen, Szu-Chieh Chen, Szu-Han Chen, Szu-Yun Chen, T Chen, Tai-Heng Chen, Tai-Tzung Chen, Tailai Chen, Tan-Zhou Chen, Tania Chen, Tao Chen, Tian Chen, Tianfeng Chen, Tianhang Chen, Tianhong Chen, Tianhua Chen, Tianpeng Chen, Tianran Chen, Tianrui Chen, Tiantian Chen, Tianzhen Chen, Tielin Chen, Tien-Hsing Chen, Ting Chen, Ting-Huan Chen, Ting-Tao Chen, Ting-Ting Chen, Tingen Chen, Tingtao Chen, Tingting Chen, Tom Wei-Wu Chen, Tong Chen, Tongsheng Chen, Tse-Ching Chen, Tse-Wei Chen, TsungYen Chen, Tuantuan Chen, Tzu-An Chen, Tzu-Chieh Chen, Tzu-Ju Chen, Tzu-Ting Chen, Tzu-Yu Chen, Tzy-Yen Chen, Valerie Chen, W Chen, Wai Chen, Wan Jun Chen, Wan-Tzu Chen, Wan-Yan Chen, Wan-Yi Chen, Wanbiao Chen, Wanjia Chen, Wanjun Chen, Wanling Chen, Wantao Chen, Wanting Chen, Wanyin Chen, Wei Chen, Wei J Chen, Wei Ning Chen, Wei-Cheng Chen, Wei-Cong Chen, Wei-Fei Chen, Wei-Hao Chen, Wei-Hui Chen, Wei-Kai Chen, Wei-Kung Chen, Wei-Lun Chen, Wei-Min Chen, Wei-Peng Chen, Wei-Ting Chen, Wei-Wei Chen, Wei-Yu Chen, Wei-xian Chen, Weibo Chen, Weican Chen, Weichan Chen, Weicong Chen, Weihao Chen, Weihong Chen, Weihua Chen, Weijia Chen, Weijie Chen, Weili Chen, Weilun Chen, Weina Chen, Weineng Chen, Weiping Chen, Weiqin Chen, Weiqing Chen, Weirui Chen, Weisan Chen, Weitao Chen, Weitian Chen, Weiwei Chen, Weixian Chen, Weixin Chen, Weiyi Chen, Weiyong Chen, Wen Chen, Wen-Chau Chen, Wen-Jie Chen, Wen-Pin Chen, Wen-Qi Chen, Wen-Tsung Chen, Wen-Yi Chen, Wenbiao Chen, Wenbing Chen, Wenfan Chen, Wenfang Chen, Wenhao Chen, Wenhua Chen, Wenjie Chen, Wenjun Chen, Wenlong Chen, Wenqin Chen, Wensheng Chen, Wenshuo Chen, Wentao Chen, Wenting Chen, Wentong Chen, Wenwen Chen, Wenwu Chen, Wenxi Chen, Wenxing Chen, Wenxu Chen, Willian Tzu-Liang Chen, Wu-Jun Chen, Wu-Xian Chen, Wuyan Chen, X Chen, X R Chen, X Steven Chen, Xi Chen, Xia Chen, Xia-Fei Chen, Xiaguang Chen, Xiameng Chen, Xian Chen, Xian-Kai Chen, Xianbo Chen, Xiancheng Chen, Xianfeng Chen, Xiang Chen, Xiang-Bin Chen, Xiang-Mei Chen, XiangFan Chen, Xiangding Chen, Xiangjun Chen, Xiangli Chen, Xiangliu Chen, Xiangmei Chen, Xiangna Chen, Xiangning Chen, Xiangqiu Chen, Xiangyu Chen, Xiankai Chen, Xianmei Chen, Xianqiang Chen, Xianxiong Chen, Xianyue Chen, Xianze Chen, Xianzhen Chen, Xiao Chen, Xiao-Chen Chen, Xiao-Hui Chen, Xiao-Jun Chen, Xiao-Lin Chen, Xiao-Qing Chen, Xiao-Quan Chen, Xiao-Wei Chen, Xiao-Yang Chen, Xiao-Ying Chen, Xiao-chun Chen, Xiao-he Chen, Xiao-ping Chen, Xiaobin Chen, Xiaobo Chen, Xiaochang Chen, Xiaochun Chen, Xiaodong Chen, Xiaofang Chen, Xiaofen Chen, Xiaofeng Chen, Xiaohan Chen, Xiaohong Chen, Xiaohua Chen, Xiaohui Chen, Xiaojiang S Chen, Xiaojie Chen, Xiaojing Chen, Xiaojuan Chen, Xiaojun Chen, Xiaokai Chen, Xiaolan Chen, Xiaole L Chen, Xiaolei Chen, Xiaoli Chen, Xiaolin Chen, Xiaoling Chen, Xiaolong Chen, Xiaolu Chen, Xiaomeng Chen, Xiaomin Chen, Xiaona Chen, Xiaonan Chen, Xiaopeng Chen, Xiaoping Chen, Xiaoqian Chen, Xiaoqing Chen, Xiaorong Chen, Xiaoshan Chen, Xiaotao Chen, Xiaoting Chen, Xiaowan Chen, Xiaowei Chen, Xiaowen Chen, Xiaoxiang Chen, Xiaoxiao Chen, Xiaoyan Chen, Xiaoyang Chen, Xiaoyin Chen, Xiaoyong Chen, Xiaoyu Chen, Xiaoyuan Chen, Xiaoyun Chen, Xiatian Chen, Xihui Chen, Xijun Chen, Xikun Chen, Ximei Chen, Xin Chen, Xin-Jie Chen, Xin-Ming Chen, Xin-Qi Chen, Xinan Chen, Xing Chen, Xing-Lin Chen, Xing-Long Chen, Xing-Zhen Chen, Xingdong Chen, Xinghai Chen, Xingxing Chen, Xingyi Chen, Xingyong Chen, Xingyu Chen, Xinji Chen, Xinlin Chen, Xinpu Chen, Xinqiao Chen, Xinwei Chen, Xinyan Chen, Xinyang Chen, Xinyi Chen, Xinyu Chen, Xinyuan Chen, Xinyue Chen, Xinzhuo Chen, Xiong Chen, Xiqun Chen, Xiu Chen, Xiu-Juan Chen, Xiuhui Chen, Xiujuan Chen, Xiuli Chen, Xiuping Chen, Xiuxiu Chen, Xiuyan Chen, Xixi Chen, Xiyao Chen, Xiyu Chen, Xu Chen, Xuan Chen, Xuancai Chen, Xuanjing Chen, Xuanli Chen, Xuanmao Chen, Xuanwei Chen, Xuanxu Chen, Xuanyi Chen, Xue Chen, Xue-Mei Chen, Xue-Qing Chen, Xue-Xin Chen, Xue-Yan Chen, Xue-Ying Chen, XueShu Chen, Xuechun Chen, Xuefei Chen, Xuehua Chen, Xuejiao Chen, Xuejun Chen, Xueli Chen, Xueling Chen, Xuemei Chen, Xuemin Chen, Xueqin Chen, Xueqing Chen, Xuerong Chen, Xuesong Chen, Xueting Chen, Xueyan Chen, Xueying Chen, Xufeng Chen, Xuhui Chen, Xujia Chen, Xun Chen, Xuxiang Chen, Xuxin Chen, Xuzhuo Chen, Y Chen, Y D I Chen, Y Eugene Chen, Y M Chen, Y P Chen, Y S Chen, Y U Chen, Y-D I Chen, Y-D Ida Chen, Ya Chen, Ya-Chun Chen, Ya-Nan Chen, Ya-Peng Chen, Ya-Ting Chen, Ya-xi Chen, Yafang Chen, Yafei Chen, Yahong Chen, Yajie Chen, Yajing Chen, Yajun Chen, Yalan Chen, Yali Chen, Yan Chen, Yan Jie Chen, Yan Q Chen, Yan-Gui Chen, Yan-Jun Chen, Yan-Ming Chen, Yan-Qiong Chen, Yan-yan Chen, Yanan Chen, Yananlan Chen, Yanbin Chen, Yanfei Chen, Yanfen Chen, Yang Chen, Yang-Ching Chen, Yang-Yang Chen, Yangchao Chen, Yanghui Chen, Yangxin Chen, Yanhan Chen, Yanhua Chen, Yanjie Chen, Yanjing Chen, Yanli Chen, Yanlin Chen, Yanling Chen, Yanming Chen, Yann-Jang Chen, Yanping Chen, Yanqiu Chen, Yanrong Chen, Yanru Chen, Yanting Chen, Yanyan Chen, Yanyun Chen, Yanzhu Chen, Yanzi Chen, Yao Chen, Yao-Shen Chen, Yaodong Chen, Yaosheng Chen, Yaowu Chen, Yau-Hung Chen, Yaxi Chen, Yayun Chen, Yazhuo Chen, Ye Chen, Ye-Guang Chen, Yeh Chen, Yelin Chen, Yen-Chang Chen, Yen-Chen Chen, Yen-Cheng Chen, Yen-Ching Chen, Yen-Fu Chen, Yen-Hao Chen, Yen-Hsieh Chen, Yen-Jen Chen, Yen-Ju Chen, Yen-Lin Chen, Yen-Ling Chen, Yen-Ni Chen, Yen-Rong Chen, Yen-Teen Chen, Yewei Chen, Yi Chen, Yi Feng Chen, Yi-Bing Chen, Yi-Chun Chen, Yi-Chung Chen, Yi-Fei Chen, Yi-Guang Chen, Yi-Han Chen, Yi-Hau Chen, Yi-Heng Chen, Yi-Hong Chen, Yi-Hsuan Chen, Yi-Hui Chen, Yi-Jen Chen, Yi-Lin Chen, Yi-Ru Chen, Yi-Ting Chen, Yi-Wen Chen, Yi-Yung Chen, YiChung Chen, YiPing Chen, Yian Chen, Yibing Chen, Yibo Chen, Yidan Chen, Yiding Chen, Yidong Chen, Yiduo Chen, Yifa Chen, Yifan Chen, Yifang Chen, Yifei Chen, Yih-Chieh Chen, Yihao Chen, Yihong Chen, Yii-Der Chen, Yii-Der I Chen, Yii-Derr Chen, Yii-der Ida Chen, Yijiang Chen, Yijun Chen, Yike Chen, Yilan Chen, Yilei Chen, Yili Chen, Yilin Chen, Yiming Chen, Yin-Huai Chen, Ying Chen, Ying-Cheng Chen, Ying-Hsiang Chen, Ying-Jie Chen, Ying-Jung Chen, Ying-Lan Chen, Ying-Ying Chen, Yingchun Chen, Yingcong Chen, Yinghui Chen, Yingji Chen, Yingjie Chen, Yinglian Chen, Yingting Chen, Yingxi Chen, Yingying Chen, Yingyu Chen, Yinjuan Chen, Yintong Chen, Yinwei Chen, Yinzhu Chen, Yiru Chen, Yishan Chen, Yisheng Chen, Yitong Chen, Yixin Chen, Yiyin Chen, Yiyun Chen, Yizhi Chen, Yong Chen, Yong-Jun Chen, Yong-Ping Chen, Yong-Syuan Chen, Yong-Zhong Chen, YongPing Chen, Yongbin Chen, Yongfa Chen, Yongfang Chen, Yongheng Chen, Yonghui Chen, Yongke Chen, Yonglu Chen, Yongmei Chen, Yongming Chen, Yongning Chen, Yongqi Chen, Yongshen Chen, Yongshuo Chen, Yongxing Chen, Yongxun Chen, You-Ming Chen, You-Xin Chen, You-Yue Chen, Youhu Chen, Youjia Chen, Youmeng Chen, Youran Chen, Youwei Chen, Yu Chen, Yu-Bing Chen, Yu-Cheng Chen, Yu-Chi Chen, Yu-Chia Chen, Yu-Chuan Chen, Yu-Fan Chen, Yu-Fen Chen, Yu-Fu Chen, Yu-Gen Chen, Yu-Han Chen, Yu-Hui Chen, Yu-Ling Chen, Yu-Ming Chen, Yu-Pei Chen, Yu-San Chen, Yu-Si Chen, Yu-Ting Chen, Yu-Tung Chen, Yu-Xia Chen, Yu-Xin Chen, Yu-Yang Chen, Yu-Ying Chen, Yuan Chen, Yuan-Hua Chen, Yuan-Shen Chen, Yuan-Tsong Chen, Yuan-Yuan Chen, Yuan-Zhen Chen, Yuanbin Chen, Yuanhao Chen, Yuanjia Chen, Yuanjian Chen, Yuanli Chen, Yuanqi Chen, Yuanwei Chen, Yuanwen Chen, Yuanyu Chen, Yuanyuan Chen, Yubin Chen, Yucheng Chen, Yue Chen, Yue-Lai Chen, Yuebing Chen, Yueh-Peng Chen, Yuelei Chen, Yuewen Chen, Yuewu Chen, Yuexin Chen, Yuexuan Chen, Yufei Chen, Yufeng Chen, Yuh-Lien Chen, Yuh-Ling Chen, Yuh-Min Chen, Yuhan Chen, Yuhang Chen, Yuhao Chen, Yuhong Chen, Yuhui Chen, Yujie Chen, Yule Chen, Yuli Chen, Yulian Chen, Yulin Chen, Yuling Chen, Yulong Chen, Yulu Chen, Yumei Chen, Yun Chen, Yun-Ju Chen, Yun-Tzu Chen, Yun-Yu Chen, Yundai Chen, Yunfei Chen, Yunfeng Chen, Yung-Hsiang Chen, Yung-Wu Chen, Yunjia Chen, Yunlin Chen, Yunn-Yi Chen, Yunqin Chen, Yunshun Chen, Yunwei Chen, Yunyun Chen, Yunzhong Chen, Yunzhu Chen, Yupei Chen, Yupeng Chen, Yuping Chen, Yuqi Chen, Yuqin Chen, Yuqing Chen, Yuquan Chen, Yurong Chen, Yushan Chen, Yusheng Chen, Yusi Chen, Yuting Chen, Yutong Chen, Yuxi Chen, Yuxian Chen, Yuxiang Chen, Yuxin Chen, Yuxing Chen, Yuyan Chen, Yuyang Chen, Yuyao Chen, Z Chen, Zan Chen, Zaozao Chen, Ze-Hui Chen, Ze-Xu Chen, Zechuan Chen, Zemin Chen, Zetian Chen, Zexiao Chen, Zeyu Chen, Zhanfei Chen, Zhang-Liang Chen, Zhang-Yuan Chen, Zhangcheng Chen, Zhanghua Chen, Zhangliang Chen, Zhanglin Chen, Zhangxin Chen, Zhanjuan Chen, Zhao Chen, Zhao-Xia Chen, ZhaoHui Chen, Zhaojun Chen, Zhaoli Chen, Zhaolin Chen, Zhaoran Chen, Zhaowei Chen, Zhaoyao Chen, Zhe Chen, Zhe-Ling Chen, Zhe-Sheng Chen, Zhe-Yu Chen, Zhebin Chen, Zhehui Chen, Zhelin Chen, Zhen Bouman Chen, Zhen Chen, Zhen-Hua Chen, Zhen-Yu Chen, Zhencong Chen, Zhenfeng Chen, Zheng Chen, Zheng-Zhen Chen, Zhenghong Chen, Zhengjun Chen, Zhengling Chen, Zhengming Chen, Zhenguo Chen, Zhengwei Chen, Zhengzhi Chen, Zhenlei Chen, Zhenyi Chen, Zhenyue Chen, Zheping Chen, Zheren Chen, Zhesheng Chen, Zheyi Chen, Zhezhe Chen, Zhi Bin Chen, Zhi Chen, Zhi-Hao Chen, Zhi-bin Chen, Zhi-zhe Chen, Zhiang Chen, Zhichuan Chen, Zhifeng Chen, Zhigang Chen, Zhigeng Chen, Zhiguo Chen, Zhihai Chen, Zhihang Chen, Zhihao Chen, Zhiheng Chen, Zhihong Chen, Zhijian Chen, Zhijian J Chen, Zhijing Chen, Zhijun Chen, Zhimin Chen, Zhinan Chen, Zhiping Chen, Zhiqiang Chen, Zhiquan Chen, Zhishi Chen, Zhitao Chen, Zhiting Chen, Zhiwei Chen, Zhixin Chen, Zhixuan Chen, Zhixue Chen, Zhiyong Chen, Zhiyu Chen, Zhiyuan Chen, Zhiyun Chen, Zhizhong Chen, Zhong Chen, Zhongbo Chen, Zhonghua Chen, Zhongjian Chen, Zhongliang Chen, Zhongxiu Chen, Zhongzhu Chen, Zhou Chen, Zhouji Chen, Zhouliang Chen, Zhoulong Chen, Zhouqing Chen, Zhuchu Chen, Zhujun Chen, Zhuo Chen, Zhuo-Yuan Chen, ZhuoYu Chen, Zhuohui Chen, Zhuojia Chen, Zi-Jiang Chen, Zi-Qing Chen, Zi-Yang Chen, Zi-Yue Chen, Zi-Yun Chen, Zian Chen, Zifan Chen, Zihan Chen, Zihang Chen, Zihao Chen, Zihe Chen, Zihua Chen, Zijie Chen, Zike Chen, Zilin Chen, Zilong Chen, Ziming Chen, Zinan Chen, Ziqi Chen, Ziqing Chen, Zitao Chen, Zixi Chen, Zixin Chen, Zixuan Chen, Ziying Chen, Ziyuan Chen, Zoe Chen, Zongming E Chen, Zongnan Chen, Zongyou Chen, Zongzheng Chen, Zugen Chen, Zuolong Chen
articles
Jincheng Miao, Chen Wang, Peiming Kuang +6 more · 2026 · Bioresource technology · Elsevier · added 2026-04-24
Enzyme immobilization is critical for enhancing enzyme stability and reusability. Catalytically active inclusion bodies (CatIBs) have emerged as a promising immobilization strategy due to their straig Show more
Enzyme immobilization is critical for enhancing enzyme stability and reusability. Catalytically active inclusion bodies (CatIBs) have emerged as a promising immobilization strategy due to their straightforward production, ease of separation, and high purity. Unlike traditional cross-linked enzyme aggregates (CLEAs) that require a precipitation step, CatIBs form through carrier-free self-aggregation during expression. To overcome the limitations of conventional methods, a novel technique has been developed in this study, focusing on L-phenylserine aldolase (LPA) as the model enzyme. A hybrid tag (HLHLHL) was fused to the N-terminus of LPA to generate 3HL-LPA, which promotes the formation of active inclusion bodies. Based on structural prediction and surface properties, the active aggregation process of 3HL tags through electrostatic interactions and hydrophobic interactions was analyzed. Innovatively, we combined CatIBs and CLEAs technologies to develop novel CatIBs-CLEAs. For comparison, a control was prepared by fusing a hexahistidine tag (HHHHHH) to LPA's N-terminus (6H-LPA) to enhance soluble expression, followed by conventional CLEAs preparation. Results showed that CatIBs-CLEAs achieved an activity recovery of 69.87% after glutaraldehyde crosslinking, significantly higher than the 48.1% for conventional CLEAs. CatIBs-CLEAs also exhibited superior thermal stability across temperatures, high stability between pH 5-9, and retained over 70% activity after seven batch cycles. The integrated CatIBs-CLEAs technology combines the production advantages of CatIBs with the stability benefits of CLEAs, offering a promising strategy for designing efficient, robust industrial biocatalysts with broad application potential. Show less
no PDF DOI: 10.1016/j.biortech.2026.134564
LPA
Lingxue Chen, Jing Yang, Li Wang +5 more · 2026 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
Based on self-determination theory (SDT), this study aimed to identify latent profiles of health motivation characteristics among young and middle-aged individuals with prediabetes and to examine thei Show more
Based on self-determination theory (SDT), this study aimed to identify latent profiles of health motivation characteristics among young and middle-aged individuals with prediabetes and to examine their associations with self-management behaviours and metabolic risk indicators. This cross-sectional study recruited individuals with prediabetes from January 2024 to January 2025 using a convenience sampling method, enrolling 309 participants. Health behaviour motivation, basic psychological needs, prediabetes-related disease knowledge and self-management were assessed using validated questionnaires. Latent profile analysis (LPA) was conducted to identify distinct subgroups. Multinomial logistic regression was used to examine demographic, lifestyle and clinical factors associated with profile membership. Three types of health motivation characteristics were identified: high psychological need satisfaction-autonomous motivation profile (24.0%), moderate psychological need satisfaction-externally controlled motivation dominant profile (15.0%) and low psychological need satisfaction-low motivation profile (61.0%). After adjustment, BMI, comorbidity history and occupation were significantly associated with profile membership, whereas distance to primary healthcare facilities showed a non-robust pattern. Significant heterogeneity exists in health motivation characteristics among young and middle-aged individuals with prediabetes, with the low psychological need satisfaction-low motivation profile representing the largest proportion. Incorporating motivation-oriented stratification into diabetes prevention strategies may provide a useful framework for delivering tailored interventions and supporting more sustainable self-management. Show less
no PDF DOI: 10.1111/dom.70726
LPA
Liang Chen, Helen C S Meier · 2026 · GeoHealth · American Geophysical Union · added 2026-04-24
This study investigated the relationships between vacant land and key adverse health behaviors, including smoking, insufficient sleep, and no leisure-time physical activity (No LPA), across census tra Show more
This study investigated the relationships between vacant land and key adverse health behaviors, including smoking, insufficient sleep, and no leisure-time physical activity (No LPA), across census tracts in Chicago, Illinois. Using both global regression and geographically weighted regression (GWR), we evaluated whether neighborhood vacant land ratios (VLRs) were associated with the prevalence of these adverse health behaviors and assessed how these associations varied spatially across the city. We found significant spatial clustering in both vacant land and health behavior indicators, and the spatial clustering patterns of neighborhood vacancy and adverse health behaviors were broadly consistent. In global models, higher VLRs were associated with higher prevalence of adverse health behaviors; after accounting for spatially autocorrelated errors, the associations remained robust for smoking and insufficient sleep but were attenuated for No LPA. GWR results further revealed clear spatial non-stationarity, with stronger positive local associations concentrated in low-income neighborhoods on the south and west sides. When overlaid with Healthy Chicago Zones (HCZs), the strong vacancy-behavior associations aligned primarily with the West, Southwest, Near South, and Far South zones, highlighting these HCZs as priority areas where vacancy was most strongly linked to adverse health behaviors. Our findings support theories of neighborhood disorder and spatial inequality, emphasizing that vacant land is a potentially modifiable environmental determinant of health behaviors and calling for tailored interventions that consider local social and economic contexts to improve community health and advance health equity. Show less
📄 PDF DOI: 10.1029/2025GH001509
LPA
Hao Jia, Yuhong Chen, Lin Dou +1 more · 2026 · Brain and behavior · Wiley · added 2026-04-24
To investigate the relationship between different physical activity (PA) patterns and stroke incidence among middle-aged and elderly populations in China. Data were drawn from the China Health and Ret Show more
To investigate the relationship between different physical activity (PA) patterns and stroke incidence among middle-aged and elderly populations in China. Data were drawn from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort encompassing 2011 to 2020. PA was calculated based on the International Physical Activity Questionnaire. Different patterns of PA included moderate-to-vigorous PA (MVPA, ≥ 150 min/wk vs. < 150 min/wk), vigorous PA (VPA, ≥ 75 min/wk vs. < 75 min/wk), moderate PA (MPA, ≥ 150 min/wk vs. < 150 min/wk), light PA (LPA, ≥ 300 min/wk vs. < 300 min/wk), and total PA (TPA, ≥ 600 metabolic equivalent of task [MET]-min/wk vs. < 600 MET-min/wk). Cox proportional hazards models evaluated stroke risk associations, while restricted cubic splines (RCS) characterized TPA dose-response effects. There were 5090 participants in total (mean age, 59.23 [standard deviation, 9.43] years; 54.5% were female), and 378 (7.4%) incident stroke cases were documented at a 9-year follow-up. Achieving the World Health Organization (WHO) guideline of ≥150 min/wk MVPA was associated with a 24% lower stroke risk (adjusted hazard ratio [HR] = 0.77, 95% confidence interval [CI] = 0.62-0.96, p = 0.019). No significant association was observed between VPA (HR = 0.79, 95% CI 0.63-1.01), MPA (HR = 0.82, 95% CI = 0.67-1.01), LPA (HR = 0.86, 95% CI = 0.70-1.07), or TPA (HR = 0.84, 95% CI = 0.65-1.08) and stroke risk. Additionally, RCS analysis demonstrated a non-significant dose-response relationship between TPA and stroke risk. This study validates WHO's MVPA guidelines (≥ 150 min/wk) for stroke prevention in Chinese elders. However, the predominantly self-reported and occupation-based PA in this cohort highlights the need for future research focusing on objective measurements of leisure-time PA. Show less
no PDF DOI: 10.1002/brb3.71316
LPA
Jiarou Chen, Kaiyue Han, Xingxing Liao +6 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
Executive function (EF) deficits are a core cognitive feature of autism spectrum disorder (ASD) and are closely associated with social responsiveness. Previous research has primarily focused on childr Show more
Executive function (EF) deficits are a core cognitive feature of autism spectrum disorder (ASD) and are closely associated with social responsiveness. Previous research has primarily focused on children with ASD, whereas how specific executive components relate to social functioning in adults remains less clear. This study examined whether patterns of association between EF and social responsiveness differ between children and adults with and without ASD. Data were obtained from the Autism Brain Imaging Data Exchange II (ABIDE II), including 423 participants aged 8-23 years (ASD = 184; controls = 239). EF was evaluated using the Behavior Rating Inventory of Executive Function (BRIEF/BRIEF-A), and social responsiveness was assessed with the Social Responsiveness Scale (SRS). Covariates of age, sex, and full-scale IQ (FIQ) were controlled using entropy balancing in children and multiple regression in adults. Hierarchical regression, moderated mediation analysis, and latent profile analysis (LPA) were conducted to examine the moderation, mediation, and heterogeneity effects, respectively. Across both child and adult samples, individuals with ASD exhibited significantly higher T-scores than controls on nearly all BRIEF and SRS subdomains after covariate adjustment (all adjusted p < 0.01), indicating widespread EF and social responsiveness impairments. Moderation analyses revealed no significant age group × EF interaction, indicating that the association between EF and social responsiveness was consistent across development. Mediation analysis revealed age-specific pathways, with EF broadly mediating social responsiveness in adults but showing more selective mediation in children. LPA identified four distinct subtypes, which were independent of age, sex, and FIQ. EF-social responsiveness associations were evident across development, but the functional contribution of specific executive components became more differentiated with age. Working memory showed greater relative prominence in adulthood. Latent profile analysis revealed heterogeneity in how executive difficulties align with social challenges, supporting developmentally informed assessment and clinical interpretation rather than direct treatment recommendations. Show less
📄 PDF DOI: 10.3389/fpsyt.2026.1729973
LPA
Yuxian Huang, Matthew Pase, Nan Hua +6 more · 2026 · Systematic reviews · BioMed Central · added 2026-04-24
The 24-h movement behavior framework includes all physical activity (PA), sedentary behavior (SB), and sleep as interdependent components of a full day. While evidence highlights the benefits of highe Show more
The 24-h movement behavior framework includes all physical activity (PA), sedentary behavior (SB), and sleep as interdependent components of a full day. While evidence highlights the benefits of higher PA, lower SB, and adequate sleep for health, the combined effects of these behaviors on mental and physical health remain unclear. This systematic review will explore the associations between 24-h movement behavior compositions and mental and physical health outcomes, providing insights for developing balanced movement behavior guidelines. This systematic review will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guideline. PubMed, PsycINFO, Embase, Web of Science, and Sport Discus will be searched for studies published between 2015 and 2025. Eligible studies must report 24-h movement behavior metrics-the composition of time allocated to sleep, sedentary behavior, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Included studies must also examine at least one mental (e.g., depression, anxiety) or physical (e.g., BMI, systolic blood pressure, all-cause mortality) health outcome. For each study, we will extract the time allocated to each behavior and effect estimates with 95% CIs (e.g., percent change in BMI, odds ratios for depression, hazard ratios for mortality) to quantify the magnitude and direction of associations. Screening, data extraction, and quality assessment will be conducted independently by two reviewers. The quality of evidence for each outcome will be assessed using the GRADE approach. Due to expected heterogeneity in study designs, a meta-analysis will not be performed. Instead, a structured narrative synthesis will be presented, stratified by age group and health condition, to summarize findings and identify key research gaps. The proposed systematic review will be the first to comprehensively review how combinations of PA, SB, and sleep are associated with mental and physical health using compositional data analysis. By emphasizing the interdependent nature of 24-h movement behaviors, the findings will provide a clearer understanding of how time spent among these behaviors influences health outcomes. The review aims to support evidence-based recommendations for optimizing daily movement behavior patterns to improve health across diverse populations. PROSPERO (CRD42023445730). Show less
no PDF DOI: 10.1186/s13643-026-03165-2
LPA
Xiaoqing Wang, Ruisen Chen, Panqin Ye +1 more · 2026 · Behavioral sciences (Basel, Switzerland) · MDPI · added 2026-04-24
This study explores the influence of congruence and incongruence in father-mother co-parenting on adolescent depression, as well as the mediating effect of self-esteem. A total of 1389 adolescents com Show more
This study explores the influence of congruence and incongruence in father-mother co-parenting on adolescent depression, as well as the mediating effect of self-esteem. A total of 1389 adolescents completed questionnaires assessing their levels of depression and self-esteem, while their fathers and mothers correspondingly reported on their own co-parenting behaviors using the Parental Co-parenting Scale in this cross-sectional study. Dates were analyzed using LPA, RSA, and mediation consecutively. The results show that: (1) We identified three distinct co-parenting profiles: positive parental co-parenting, negative parental co-parenting, and mixed parental co-parenting. (2) In cases of congruent parental co-parenting, high positive parental co-parenting was associated with lower adolescent depression, whereas high negative parental co-parenting was linked to higher depression, and the difference manifests in different forms among boys and girls. Girls showed nonlinear changes in depression while boys exhibited linear trends. (3) In cases of incongruence in parental co-parenting, mothers' co-parenting exerted a stronger influence on boys' depression, while girls were not affected by mothers' and fathers' discrepancies. (4) Self-esteem mediated the relationship between parental co-parenting (in)congruence and depression across both genders. This study provides evidence for the mechanism through which parental coparenting influences adolescent depression and offers a basis for future interventions targeting adolescent depression. Show less
📄 PDF DOI: 10.3390/bs16030448
LPA
Chao-Yun Cheng, Yih-Jer Wu, Chih-Fan Yeh +25 more · 2026 · Journal of the Formosan Medical Association = Taiwan yi zhi · Elsevier · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic Show more
Lipoprotein(a) [Lp(a)] is a genetically determined lipoprotein that has been established as an independent and causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve disease (CAVD). Structurally composed of a low-density lipoprotein (LDL)-like particle covalently linked to apolipoprotein(a) [apo(a)], Lp(a) exhibits unique atherogenic, thrombogenic, and inflammatory properties, largely due to its role as a carrier of oxidized phospholipids (OxPL). Plasma Lp(a) concentrations are predominantly determined by the number of kringle IV type 2 (KIV-2) repeats in the LPA gene, with minimal influence from lifestyle or environmental factors. Despite substantial evidence linking elevated Lp(a) to cardiovascular risk, clinical testing remains underutilized, especially in East Asian countries. In Taiwan, although population-level Lp(a) concentrations are comparatively low, a significant subset exceeds risk thresholds, with local studies confirming its prognostic value in coronary artery disease and ischemic stroke. Barriers, including limited physician awareness, implementation barriers, and therapeutic nihilism, contribute to its under-recognition. This review highlights the molecular features of Lp(a), its pathogenesis of cardiovascular disorders, epidemiology, and current barriers and future advances in diagnostic testing, with a particular focus on implications for cardiovascular risk management in Taiwan. Show less
no PDF DOI: 10.1016/j.jfma.2026.03.073
LPA
Huan Huang, Zhaojun Chen, Jiong Liu +4 more · 2026 · Journal of epidemiology and community health · added 2026-04-24
Older adults typically have higher sedentary behaviour (SB) and lower physical activity (PA) than younger adults. Studies on replacing SB with PA in relation to all-cause mortality in racially diverse Show more
Older adults typically have higher sedentary behaviour (SB) and lower physical activity (PA) than younger adults. Studies on replacing SB with PA in relation to all-cause mortality in racially diverse older adults remain limited. This study included 122 966 older adults from the China Kadoorie Biobank (CKB) and 207 212 older adults from the UK Biobank (UKB). SB and PA were assessed using baseline questionnaires, with PA classified as light (LPA), moderate (MPA) or vigorous (VPA) based on metabolic equivalents. Cox proportional hazards models and isotemporal substitution models were used to examine the associations between replacing SB with different PA intensities and all-cause mortality. Longer SB (per 30 min/day increase) was associated with a higher risk of all-cause mortality in both cohorts (CKB: HR 1.013, 95% CI 1.010 to 1.017; UKB: HR 1.012, 95% CI 1.009 to 1.015). PA of any intensity was associated with a reduced risk of all-cause mortality. In the CKB, replacing 30 min/day of SB with an equivalent duration of PA showed comparable protective associations (LPA: HR 0.963, 95% CI 0.958 to 0.968; MPA: HR 0.967, 95% CI 0.961 to 0.972; VPA: HR 0.965, 95% CI 0.960 to 0.971). In the UKB, replacing 30 min/day of SB with VPA was associated with the largest reduction in mortality risk (HR: 0.950, 95% CI 0.931 to 0.970). Replacing SB with PA of any intensity was associated with reduced all-cause mortality risk in older adults, with variations across populations. These findings highlight the need for population-specific PA recommendations to promote healthy ageing. Show less
no PDF DOI: 10.1136/jech-2025-225695
LPA
Fang Chen, Juan Gao · 2026 · Journal of multidisciplinary healthcare · added 2026-04-24
This study aimed to identify distinct latent profiles (categories) of health behavior protection motivation among patients with type 2 diabetes using latent profile analysis. Subsequently, we compared Show more
This study aimed to identify distinct latent profiles (categories) of health behavior protection motivation among patients with type 2 diabetes using latent profile analysis. Subsequently, we compared e-health literacy levels across these patient categories and analyzed factors influencing protection motivation. The findings are intended to provide a scientific basis for precise diabetes management. From January to March 2025, a cross-sectional survey was conducted on a convenience sample of 253 patients, and data were collected using relevant scales such as the health-related behavior protection motivation assessment. LPA was performed using Mplus 8.3 to identify motivational profiles. Binary logistic regression was applied to determine influencing factors. A total of 253 valid questionnaires were collected. Two latent profiles of health-related behavioral protection motivation were identified: the "high perceived cost-incentive-dependent group" (n = 91, 35.97%) and the "high sensitivity-high efficacy group" (n = 162, 64.03%). The total eHealth literacy score of the "high perceived cost-incentive-dependent group" was 100.35 ± 17.89, which was significantly lower than that of the "high sensitivity-high efficacy group" (110.76 ± 13.78), with a statistically significant difference (t = -5.165, P < 0.001). Logistic regression analysis revealed that patients who monitored their blood glucose more than three times per week were 2.95 times more likely to have a higher level of protective motivation compared to those who did so three times or fewer (95% CI: 1.679-5.197, P < 0.001). There is population heterogeneity in health-related behavioral protection motivation among patients with type 2 diabetes. Frequency of blood glucose monitoring per week was identified as influencing factors of motivational profile membership. Differences in eHealth literacy levels were also observed between the two groups. Targeted interventions should be provided based on population characteristics to enhance motivation, improve electronic health literacy, and behavioral compliance. Show less
📄 PDF DOI: 10.2147/JMDH.S576440
LPA
Jiaqi Zuo, Jie Zhang, Ying Tang +10 more · 2026 · The Plant cell · Oxford University Press · added 2026-04-24
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties Show more
Phytate (phytic acid, or InsP6), the primary phosphorus storage compound in plants, plays essential roles in nutrient homeostasis and cellular signaling. However, its strong metal-chelating properties make cytosolic accumulation cytotoxic, necessitating its sequestration into vacuoles for safe storage. Here, we present the cryo-EM structures of the rice vacuolar phytate transporter, OsMRP5, captured in distinct functional states. These structures reveal the molecular basis of OsMRP5 function as an ATP-binding cassette (ABC) transporter. OsMRP5 employs a specialized substrate-recognition mechanism, uniquely adapted to bind the fully hydrophilic InsP6 through extensive electrostatic and hydrogen-bonding interactions within two distinct, highly polar binding sites in its central cavity. A distinctive electropositive tunnel, positioned above the central cavity, forms a continuous pathway connecting the InsP6-binding pocket to the vacuolar export site. This tunnel likely generates an electrostatic attraction that facilitates the movement of the highly anionic InsP6 through the transporter. By mapping mutations from low-phytic acid (lpa) crop variants onto the OsMRP5 structures, we pinpoint their conserved locations critical for transporter function and validate their impact experimentally. These results reveal how OsMRP5 recognizes and transports the highly charged InsP6 molecules into vacuoles, providing a molecular framework for targeted manipulation of this agriculturally important transporter. Show less
no PDF DOI: 10.1093/plcell/koag088
LPA
Mengyun Luo, Philip James Clare, Jakob Tarp +8 more · 2026 · British journal of sports medicine · added 2026-04-24
We employed a causal inference framework to estimate the counterfactual dose-response effects of light-intensity physical activity (LPA) on mortality across low, medium and high moderate- to vigorous- Show more
We employed a causal inference framework to estimate the counterfactual dose-response effects of light-intensity physical activity (LPA) on mortality across low, medium and high moderate- to vigorous-intensity physical activity (MVPA) levels, and the lower and higher thresholds of current MVPA recommendations. Eligible participants from the UK Biobank (n=71 715) were included in the current study. LPA and MVPA were measured via accelerometers, and mortality data were derived from death registry. Flexible parametric survival models were used under the counterfactual framework to estimate the marginal predicted probability of death after 10 years of follow-up. During a median follow-up period of 8.0 years, 2195 deaths occurred. A non-linear dose-response effect of LPA on all-cause mortality was evident, and the effect diminished as MVPA level increased. If all participants achieved the lower threshold of the WHO recommended 22 min/day of MVPA, the 10-year probability of death would be expected to decrease from 9.5% at 60 min/day LPA to 4.2% at 360 min/day. If all participants achieved the higher threshold of 44 min/day of MVPA, the 10-year probability of death would be expected to decrease from 6.6% at 60 min/day of LPA to 3.7% at 345 min/day. Across the MVPA values examined, the optimal dose for LPA ranged from 195 to 225 min/day. LPA may complement MVPA to reduce risk of all-cause mortality, particularly among those with low MVPA or those unable to engage in higher-intensity activities. Our study highlights the potential for integrating LPA into public health strategies and future physical activity guidelines. Show less
no PDF DOI: 10.1136/bjsports-2025-110782
LPA
Shang Gao, Qiyuan Wang, Keyao Kang +3 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Job satisfaction is a critical factor influencing workplace efficiency and employee well-being. In the context of Industry 5.0 transformation, understanding the latent profiles of job satisfaction and Show more
Job satisfaction is a critical factor influencing workplace efficiency and employee well-being. In the context of Industry 5.0 transformation, understanding the latent profiles of job satisfaction and their relationship with mental health outcomes, such as depression, anxiety, and digital-intelligence job insecurity, is critical for promoting employee well-being and organizational sustainability. This study aims to explore the latent profiles of job satisfaction among industrial workers and explore their associations with mental health outcomes. This study used cross-sectional data from 3,420 male frontline workers from a large automobile manufacturing enterprise in Jilin Province, China in April 2024. Latent profile analysis (LPA) was employed to identify distinct latent profiles of job satisfaction among industrial workers, while hierarchical linear regression analysis was used to analyze the relationship between job satisfaction and psychological health outcomes (depression, anxiety and digital-intelligence job insecurity). The score of job satisfaction among industrial workers in Jilin Province was 3.62 ± 0.90. Four profiles were identified: very low (5.97%), low-to-moderate (31.14%), moderately high (42.63%), and high job satisfaction (20.26%). Depression and anxiety showed a clear level-gradient pattern across profiles, whereas digital-intelligence job insecurity displayed a non-monotonic pattern with higher levels in the low-to-moderate and moderately high profiles. Work stress showed consistent associations with all outcomes, and job satisfaction profiles remained associated with depression and anxiety after covariate and stress adjustment; associations with digital-intelligence job insecurity were smaller but detectable. This study examined heterogeneity in job satisfaction among frontline industrial workers and its associations with mental health outcomes. Latent profile analysis identified four job satisfaction profiles. Job satisfaction profile membership remained strongly associated with depression and anxiety. Digital-intelligence job insecurity showed a non-monotonic pattern across profiles. These findings suggest that an individual-centered profile approach provides actionable differentiation of mental health symptom burden across distinct job satisfaction patterns, supporting more targeted workplace strategies. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1772767
LPA
Chuqin Xiong, Shuge Wang, Peiran Guo +6 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantl Show more
Nursing interns often face maladjustment during the early stages of clinical practice, which not only directly affects their physical and mental health as well as work efficiency but also significantly inhibits their proactive feedback-seeking behavior (FSB). As an active self-regulation strategy, FSB can enhance interns' work initiative and promote role transition. However, existing research has yet to thoroughly investigate the potential heterogeneity and categorical characteristics of FSB within this population, and the role of psychological resources such as career adaptability in shaping these patterns requires further investigation. To investigate the status of FSB in early-stage nursing interns, identify latent subgroups via latent profile analysis (LPA), and analyze associated factors, thereby providing evidence for targeted clinical educational interventions. Multicenter cross-sectional research. This study employed a multistage stratified cluster sampling to survey 1,308 early-stage nursing interns from nine universities in Hubei, China, between June and September 2024. Data were collected using a demographic questionnaire, Feedback-Seeking Behavior Scale, and Career Adapt-Abilities Scale. LPA was employed to delineate FSB profiles and multivariate logistic regression analysis to examine the associated predictors. A total of 1,370 questionnaires were distributed, with 1,308 valid responses, yielding an effective response rate of 95.47%. The mean score on the feedback-seeking behavior scale was 5.06 ± 1.08. LPA identified three distinct feedback-seeking profiles: low (20.87%), moderate (38.3%), and high (40.83%). Education level, student cadre experience, internship hospital type, and career adaptability were significant predictors of profile membership ( FSB among early-stage nursing interns exhibited heterogeneity. Nursing educators and managers should implement tiered interventions: for the low and moderate feedback-seeking groups, career guidance and feedback awareness cultivation should be strengthened; for the high feedback-seeking group, peer modeling should be encouraged. This strategy can enhance proactive FSB, supports role transition and professional identity, and promotes long-term nursing workforce stability. Show less
📄 PDF DOI: 10.3389/fmed.2026.1664329
LPA
Niuniu Zhou, Yuzhong Gu, Jianyun Liu +4 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
To identify latent classes based on symptom clusters and to explore the association between these distinct symptom experience subtypes and social isolation in older adults with comorbid diabetes melli Show more
To identify latent classes based on symptom clusters and to explore the association between these distinct symptom experience subtypes and social isolation in older adults with comorbid diabetes mellitus (DM) and coronary heart disease (CHD). A cross-sectional study was conducted among 337 older adults with DM and CHD recruited from the Department of Endocrinology and Cardiology of Nantong Sixth People's Hospital between February 2023 and October 2025. Data were collected using a general information questionnaire, the Chinese version of the Memorial Symptom Assessment Scale (MSAS), and the Lubben Social Network Scale-6 (LSNS-6). Exploratory factor analysis (EFA) was used to identify symptom clusters. Latent profile analysis (LPA) was then employed to classify patients into different symptom experience subtypes based on the symptom cluster scores. One-way ANOVA, Chi-square tests, and multiple linear regression were used to analyze the association between latent classes and social isolation. EFA extracted three symptom clusters (cardiopulmonary-fatigue, emotional-perceptual, and metabolic), accounting for 62.3% of the total variance. LPA identified three distinct latent classes: Class 1 "Low Burden-Balanced Pattern" (45.4%), Class 2 "Psycho-Somatic Co-dominant Pattern" (31.8%), and Class 3 "Metabolic-Physical Dominant Pattern" (22.8%). Univariate analysis revealed significant differences in social isolation scores (LSNS-6) across the three classes ( The findings reveal significant heterogeneity in symptom experiences among older adults with comorbid DM and CHD, which can be categorized into distinct latent classes. The subtype characterized by a Psycho-Somatic Co-dominant Pattern shows the strongest association with social isolation. In clinical practice, early identification of this high-burden subgroup may facilitate the provision of integrated interventions that address physical, psychological, and social dimensions. Show less
📄 PDF DOI: 10.3389/fmed.2026.1756120
LPA
Yesheng Ling, Yang Chen, Xianguan Yu +1 more · 2026 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
To assess the predictive value of serum lipoprotein(a) [Lp(a)] for contrast-induced nephropathy in patients with type 2 diabetes mellitus (T2DM). Consecutive T2DM patients who underwent coronary angio Show more
To assess the predictive value of serum lipoprotein(a) [Lp(a)] for contrast-induced nephropathy in patients with type 2 diabetes mellitus (T2DM). Consecutive T2DM patients who underwent coronary angiography (CAG) or percutaneous coronary intervention (PCI) between January 2019 and December 2021 were enrolled. Baseline Lp(a) was measured before the operation. CIN was defined as an increase in serum creatinine of more than 25% or 44 μmol within 72 h of contrast administration. The relationship between Lp(a) and CIN risk was analyzed. A total of 928 T2DM patients were included. CIN developed in 11.1% (103/928) of patients. The Lp(a) level was significantly higher in patients with CIN than in non-CIN patients (311.12 ± 278.66 vs. 254.19 ± 274.56 mg/L, A higher serum Lp(a) level indicates an increased risk of CIN in T2DM patients undergoing CAG or PCI and can serve as an independent predictor of CIN in this population. This study's findings will aid in the clinical prevention and treatment of contrast agent-induced kidney disease. Show less
📄 PDF DOI: 10.3389/fcvm.2026.1733119
LPA
Yanwei Yin, Xiaorong Chen, Chongzeng Bi +1 more · 2026 · Acta psychologica · Elsevier · added 2026-04-24
This study, adopting a person-centered approach and using network analysis, explores latent subtypes of Junzi personality among college students and their links to Receptiveness to Opposing Views, off Show more
This study, adopting a person-centered approach and using network analysis, explores latent subtypes of Junzi personality among college students and their links to Receptiveness to Opposing Views, offering empirical backing for the ancient Chinese idea of "Junzi harmonize yet remain distinct." Traditional variable-centered methods often fail to fully expose the underlying typological structure due to the possible heterogeneous combinations in Junzi personality dimensions. Thus, a person-centered latent profile analysis (LPA) was used to pinpoint typical personality trait patterns. With 1116 college students as participants, the study employed the Junzi Personality Questionnaire Based on Confucian Thought and the Receptiveness to Opposing Views Scale. LPA identified three personality types: The Moderate Type (50%), The Daring-Aggressive Type (15%), and The Virtuously-Accomplished Type (35%). Regression analysis showed significant correlations between gender, age, and personality type, with The Virtuously-Accomplished Type scoring notably higher in Receptiveness to Opposing Views. Network analysis further revealed distinct differences in the network structures of Receptiveness to Opposing Views among the three types: The Moderate Type centered on "derogation of opponents," "refraining from what should not be done," and "respectfulness and propriety"; The Daring-Aggressive Type focused on "conversancy with righteousness and cherishment of benign rule," "derogation of opponents," and "respectfulness and propriety"; while The Virtuously-Accomplished Type highlighted "negative emotions" and "wisdom, benevolence, and courage," with "taboo issues" at the periphery in all datasets. The findings uncover the heterogeneity of Junzi personality and its varied associations with Receptiveness to Opposing Views, providing insights for understanding harmonious interactions in diverse settings. Show less
no PDF DOI: 10.1016/j.actpsy.2026.106577
LPA
Yanhua Qi, Si Chen, Ziwen Pan · 2026 · BMC public health · BioMed Central · added 2026-04-24
Snacktivity—brief, high-frequency bouts of moderate-to-vigorous physical activity (MVPA) integrated into daily routines—may interrupt prolonged sitting and help accumulate total activity. Step count i Show more
Snacktivity—brief, high-frequency bouts of moderate-to-vigorous physical activity (MVPA) integrated into daily routines—may interrupt prolonged sitting and help accumulate total activity. Step count is a practical proxy for this pattern, yet the cadence thresholds that map short-bout stepping to MVPA and the relevance of bout–cadence patterns to adiposity remain unclear. This study aimed to examine the associations between accelerometer-derived step metrics and adiposity and to identify pragmatic step-based thresholds in older women. We conducted a cross-sectional study of 1,109 community-dwelling older women in Yantai, Shandong Province, China, with a mean age of 64.93 years (SD = 2.82). Step-based metrics (daily steps, MVPA and light-intensity physical activity (LPA) steps, cadence, and bout patterns) were derived from a waist-worn triaxial accelerometer. adiposity was defined using body-fat-ratio (BFR) categories assessed by multi-frequency bioelectrical impedance analysis. Multiple linear regression estimated associations with progressive adjustment for sociodemographic, lifestyle, and health-related covariates, with additional adjustment for total sedentary time. Sensitivity analyses replaced BFR with BMI and examined visceral fat mass (VFM) using linear regression. Receiver operating characteristic (ROC) analyses identified pragmatic step and cadence cut-points. MVPA step counts and cadence were consistently and inversely associated with adiposity ( Among older women, MVPA-oriented step metrics—particularly ~ 1,846 MVPA steps/day and ~ 94.3 steps/min cadence—showed inverse associations with adiposity and outperformed LPA metrics. These thresholds may serve as pragmatic, low-barrier activity targets, but causal relationships require confirmation in longitudinal and experimental studies. The online version contains supplementary material available at 10.1186/s12889-026-26912-5. Show less
📄 PDF DOI: 10.1186/s12889-026-26912-5
LPA
XiaoSong Pei, Fei Wang, Xiaomin Liu +7 more · 2026 · Oncogene · Nature · added 2026-04-24
High-grade serous ovarian cancer (HGSC) is the most aggressive subtype of ovarian epithelial cancer (OEC), with characters of late-stage diagnosis, high recurrence rate, and poor survival outcomes. Fu Show more
High-grade serous ovarian cancer (HGSC) is the most aggressive subtype of ovarian epithelial cancer (OEC), with characters of late-stage diagnosis, high recurrence rate, and poor survival outcomes. Fucosyltransferase 8 (FUT8) is responsible for α1,6-core fucosylation biosynthesis, and aberrant FUT8/α1,6-core fucosylation level is involved in tumor progression. However, the roles and mechanisms of protein FUT8 and α1,6-core fucosylation in HGSC tumorigenesis and progression remain elusive. Here, our study confirms that elevated levels of FUT8/α1,6-core fucose in the tissues and serum of HGSC patients, and the elevation is associated with poor patient prognosis. By applying glycoproteomic assay, we globally screen and identify NCEH1 as the specific scaffold protein of α1,6-core fucosylation. Alpha 1,6-core fucose modification stabilizes NCEH1 by preventing its degradation through proteasomal pathway. Importantly, combined with non-targeted metabolomics analysis, α1,6-core fucosylated NCEH1 facilitates LPA secretion, driving M2-like polarization of tumor-associated macrophages in the tumor microenvironment, thus leading to oncogenesis and peritoneal metastasis of HGSC in vitro and in vivo. These findings broaden the understanding of FUT8/α1,6-core fucosylation/NCEH1 in HGSC progression and metastasis, and offer glycosylated diagnostic indicators and targets for therapeutic strategies in HGSC. Show less
📄 PDF DOI: 10.1038/s41388-026-03703-1
LPA
Li He, Wen-Wen Yu, Hao-Tian Zheng +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Hemodialysis, as one of the main alternative treatment methods for end-stage renal disease, has received much attention in recent years. Due to the particularity of hemodialysis treatment, patients ha Show more
Hemodialysis, as one of the main alternative treatment methods for end-stage renal disease, has received much attention in recent years. Due to the particularity of hemodialysis treatment, patients have a relatively high risk of infection during the treatment process. Hemodialysis nurses, who are the main executors of the treatment operations and have the most contact with patients, have a close relationship with the infection risk of patients. The level of their hospital infection prevention and control literacy is closely related to the infection risk of patients. To explore the current level of knowledge, attitudes, and practices (KAP) of hospital infection prevention and control among haemodialysis nurses in the Sichuan Province, China, and identified their potential categories. This provided evidence-based recommendations for improving infection control management in hemodialysis departments. A cross-sectional study was conducted From July 15 to August 15, 2025 using a convenience sampling method to survey 470 hemodialysis nurses from 78 hospitals in Sichuan Province. Participants were licensed nurses with over 3 months of hemodialysis experience. Data were collected using the A total of 460 valid questionnaires were collected, with an effective response rate of 97.87%. The average scores for knowledge, attitudes, and practices related to hospital infection prevention and control among haemodialysis nurses were 4.67 ± 0.43, 4.59 ± 0.43, and 4.74 ± 0.34, respectively. Three latent profile models were constructed, with the two-class model identified as the optimal solution, which were defined as the "Low KAP Group" (25.9%) and "High KAP Group" (74.1%). Logistic regression analysis revealed that sex, responsibility for infection control, hospital level, annual number of infection control training sessions, organizational support, and work engagement were significant influencing factors ( The KAP level of haemodialysis nurses in hospital infection prevention and control was relatively high. Hospital managers should tailor supportive work environments on the basis of the individual characteristics and work engagement of haemodialysis nurses to improve the KAP level of nosocomial infection prevention and control among haemodialysis nurses. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1734891
LPA
Shaowu Xiao, Mengya Zeng, Junru He +2 more · 2026 · International journal of cardiology. Cardiovascular risk and prevention · Elsevier · added 2026-04-24
Coronary artery calcification (CAC) signifies advanced atherosclerosis and portends increased cardiovascular risk. Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerosis; however, its asso Show more
Coronary artery calcification (CAC) signifies advanced atherosclerosis and portends increased cardiovascular risk. Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerosis; however, its association with in vivo lesion morphology and clinical outcomes in patients with symptomatic, advanced CAC remains incompletely characterized. This study aimed to investigate the association between elevated Lp(a) levels and both in vivo lesion morphology and clinical outcomes in this high-risk population. In this retrospective cohort, 292 patients with intravascular ultrasound(IVUS)-confirmed CAC were stratified into elevated (≥50 mg/dL,n = 77) or low (<50 mg/dL,n = 215) Lp(a) groups. The primary endpoint was major adverse cardiovascular events (MACEs). Associations were assessed via multivariable Cox models adjusted for clinical covariates. Patients in the elevated Lp(a) group presented a greater incidence of aortic valve calcification (p < 0.001). IVUS revealed constrictive remodeling with a smaller lumen and vessel dimensions. During a median follow-up of 17.2 months, the elevated Lp(a) cohort had a significantly higher MACE rate (37.7% vs. 15.8%; adjusted hazard ratio [aHR] 2.60, 95% CI 1.55-4.35, p < 0.001). Elevated Lp(a) independently predicted increased risks of ischemic stroke (aHR 7.14) and in-stent restenosis (aHR 2.78). In symptomatic patients with IVUS-confirmed CAC, elevated Lp(a) identifies a high-risk phenotype characterized by constrictive vascular remodeling and a markedly increased risk of MACEs, driven particularly by ischemic stroke and in-stent restenosis. These findings support the integration of routine Lp(a) testing into the risk stratification of patients with severe CAC, thereby identifying a precise high-risk phenotype that warrants intensified monitoring and represents an ideal target for emerging Lp(a)-lowering therapies. Show less
📄 PDF DOI: 10.1016/j.ijcrp.2026.200606
LPA
Xinyi Ma, Yang Xu, Yeqi Nian +9 more · 2026 · American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · Elsevier · added 2026-04-24
Carboxymethylcellulose (CMC), a common food emulsifier, induces microbiota dysbiosis and systemic inflammation; however, its impact on transplant immunity remains unclear. Allogenic heart rejection wa Show more
Carboxymethylcellulose (CMC), a common food emulsifier, induces microbiota dysbiosis and systemic inflammation; however, its impact on transplant immunity remains unclear. Allogenic heart rejection was observed in CMC-fed recipient mice, with increased abundance of lysophosphatidic acid (LPA)-producing bacteria and increased serum LPA concentration. CMC-induced transplant rejection was caused by the gut microbiota, as confirmed by fecal microbiota transplantation and gut microbiota depletion. Furthermore, LPA-treated macrophages demonstrated a proinflammatory ability to accelerate allograft rejection in cytotoxic T lymphocyte-associated protein 4 immunoglobulin-induced allograft survival by upregulating glycolysis. Conversely, the administration of a glycolysis inhibitor resulted in allograft survival and abrogated the detrimental effect of LPA. Mass spectrometry and single-cell RNA sequencing confirmed that transplant patients with rejection showed significantly elevated serum LPA levels and LPA receptor 6 (LPAR6) expression in graft-infiltrate macrophages. Mechanistically, LPA preferentially promoted LPAR6 expression, which interacted with Rho-associated protein kinase 2 to activate the mammalian target of rapamycin/hypoxia-inducible factor 1-alpha pathway, thereby enhancing glycolysis and inducing proinflammatory macrophage polarization. Treatment with Ki16425, an LPAR antagonist, prolonged allograft survival in CMC-fed recipients. Our findings reveal a major detrimental effect of CMC on macrophage physiology and suggest that controlling LPAR6 expression or glycolysis in macrophages may improve allograft survival in transplant recipients. Show less
no PDF DOI: 10.1016/j.ajt.2026.02.030
LPA
Bin Ma, Jingjing Wang, Mengyuan Zhang +2 more · 2026 · BMC nursing · BioMed Central · added 2026-04-24
To evaluate the current status and latent profiles of caregiver self-care contributions for patients with chronic obstructive pulmonary disease (COPD) and examine the associations between demographic Show more
To evaluate the current status and latent profiles of caregiver self-care contributions for patients with chronic obstructive pulmonary disease (COPD) and examine the associations between demographic characteristics, health literacy, confidence in self-care contributions, family intimacy, and profile membership. We recruited 275 dyads of patients with COPD and their family caregivers from five tertiary hospitals between May and November 2022 using convenience sampling. Latent profile analysis (LPA) was used to identify distinct profiles of caregiver self-care contributions. Univariate analysis and multinomial logistic regression were subsequently conducted to examine associations between participant characteristics and profile membership. LPA identified four distinct profiles of caregiver self-care contributions: low-contributing, under-monitored, maintenance-prioritized, and high-contributing. Significant differences were observed across these profiles in terms of patients' symptom severity, exacerbation frequency, number of hospitalizations, caregivers' education levels, caregiving duration, health literacy, confidence in self-management contributions, and family intimacy using univariate analysis. Multinomial logistic regression analysis revealed that caregivers' education levels, caregiving duration, confidence in self-management contributions, and health literacy were significant predictors of profile membership. Caregiver self-care contributions for patients with COPD can be characterized by four distinct profiles, with caregivers' educational level, health literacy, and confidence in self-management identified as key factors associated with profile membership. Show less
📄 PDF DOI: 10.1186/s12912-026-04503-4
LPA
Xiaozhao Lu, Ziyao Yuan, Xiaoyu Lin +13 more · 2026 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to Show more
Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to investigate the combined effect of elevated Lp(a) and DM on survival outcomes in CAD patients. This study included 65 547 CAD patients (62.6 ± 10.7 years, 27.7% female) from CIN-II and RED-CARPET cohorts. Patients were stratified into four groups by Lp(a) levels (< or ≥ 30 mg/dL) and DM status. Multivariable Cox regression models estimated associations with cardiovascular and all-cause mortality, examining additive and multiplicative interactions. During a median follow-up of 5.5 years, 10 686 (16.3%) patients died from all causes and 5106 (7.8%) died from cardiovascular causes. Patients with Lp(a) ≥ 30 mg/dL and DM were independently associated with cardiovascular mortality (adjusted hazard ratio [aHR]: 1.28, 95% CI: 1.20-1.35; aHR: 1.53, 95% CI: 1.44-1.62, all p < 0.001, respectively). Compared to patients with Lp(a) < 30 mg/dL without DM, the aHRs were 1.26 (95% CI: 1.16-1.36, p < 0.001), 1.51 (95% CI: 1.40-1.62, p < 0.001) and 2.00 (95% CI: 1.83-2.18, p < 0.001) for those with Lp(a) ≥ 30 mg/dL without DM, Lp(a) < 30 mg/dL with DM and Lp(a) ≥ 30 mg/dL with DM, respectively. Significant additive interaction between elevated Lp(a) and DM on cardiovascular mortality was observed, with 12% of the excess risk attributed. Similar associations were observed in all-cause mortality. In patients with CAD, elevated Lp(a) and DM act synergistically to increase the risk of cardiovascular and all-cause mortality, suggesting that both risks should be considered to integrate management. Show less
no PDF DOI: 10.1111/dom.70603
LPA
Miaomiao Chen, Shailing Ma, Xiaohui Liu +5 more · 2026 · Frontiers in reproductive health · Frontiers · added 2026-04-24
China's total fertility rate has reached a critically low level, dropping to approximately 1.0 by the end of 2023which is significantly below the population replacement level of 1.5. This decline refl Show more
China's total fertility rate has reached a critically low level, dropping to approximately 1.0 by the end of 2023which is significantly below the population replacement level of 1.5. This decline reflects a marked reduction in fertility intention among reproductive-aged women, exacerbating population aging and threatening long-term labor supply and social sustainability. Despite policy adjustments and governmental support initiatives, intended outcomes have not been realized. Current literature largely focuses on isolated determinants of fertility intention, overlooking heterogeneity within the population. Moreover, the pathways through which psychosocial factors operate across different subgroups remain poorly understood. Data for this study were derived from the 2021 Psychological and Behavioral Investigation of Chinese Residents (PBICR 2021), a nationally representative cross-sectional survey. Latent profile analysis (LPA) was employed to identify subtypes of fertility intention among reproductive-aged women, followed by multinomial logistic regression, which examined factors associated with different profiles. Among 2,973 reproductive-aged female participants, three distinct fertility intention profiles were identified via latent profile analysis: the Fertility Intention Decline Group (25.1%), the Low Fertility Intention Group (51.3%), and the High Fertility Intention Group (23.6%). Multinomial logistic regression analysis revealed that, compared with the Fertility Intention Decline Group, the Low Fertility Intention Group was significantly associated with family type, aged 20-40 years, residential location, having 2 children, and retirement status (all Fertility intention among reproductive-aged women demonstrates significant heterogeneity. This study identified three distinct latent profiles, each characterized by unique patterns of influencing factors. The findings highlight the necessity of moving beyond one-size-fits-all policy approaches and emphasize the importance of developing tailored interventions that account for the specific characteristics and determinants of each subgroup. Show less
📄 PDF DOI: 10.3389/frph.2026.1758039
LPA
Ashen L Vidanage, Tianyu Xu, Zihao Chen +9 more · 2026 · International journal of cardiology. Cardiovascular risk and prevention · Elsevier · added 2026-04-24
Serum lipoprotein(a) [Lp(a)] is recognized as an independent risk factor for cardiovascular disease. However, whether hypertension modifies the association between Lp(a) and adverse outcomes in acute Show more
Serum lipoprotein(a) [Lp(a)] is recognized as an independent risk factor for cardiovascular disease. However, whether hypertension modifies the association between Lp(a) and adverse outcomes in acute decompensated heart failure (ADHF) remains unclear. We investigated how hypertension status influences the relationship between Lp(a) and all-cause mortality in ADHF. We conducted a single-center retrospective observational study including 2610 patients hospitalized with ADHF. We normalized the distribution of Lp(a) by a logarithmic transformation and assessed the risk of all-cause mortality with Lp(a), using Cox regression with adjustment for potential confounders. Among 2610 patients (39.0% women; mean age, 68.8 years), 1606 (61.5%) had hypertension. Over 4.1 years (median), 1287 deaths occurred. In all patients, log-transformed Lp(a) was significantly associated with mortality (adjusted HR 1.21; 95% CI, 1.05-1.39; Increased admission Lp(a) levels were associated with a higher risk of all-cause mortality in ADHF patients with hypertension. Further studies are needed to explore the mechanistic links among Lp(a), hypertension and ADHF. Show less
📄 PDF DOI: 10.1016/j.ijcrp.2026.200594
LPA
Bin Yang, Long Yin, Zongyu Yang +4 more · 2026 · Journal of exercise science and fitness · Elsevier · added 2026-04-24
This study aims to identify the 24-h movement behavior patterns of preschool children using Latent Profile Analysis based on Compositional Data Analysis (CoDA), and to examine their associations with Show more
This study aims to identify the 24-h movement behavior patterns of preschool children using Latent Profile Analysis based on Compositional Data Analysis (CoDA), and to examine their associations with physical fitness. The study employs a cross-sectional design. A total of 329 healthy children aged 4-6 years were selected. Accelerometers (ActiGraph wGT3-BT, Pensacola, FL, USA) were used to measure light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sedentary behavior (SB), while sleep was assessed through parent and teacher questionnaires. The assessment of physical fitness was conducted in accordance with the "Chinese National Physical Fitness Test Standards" (Preschooler Section). To address the multicollinearity problems among components of physical activity (PA), CoDA was first applied, subsequently, Latent Profile Analysis was utilized to categorize 24-h movement behavior patterns, while a Generalized Ordered Logit Model (GOLM) was applied to investigate their associations with physical fitness. Three distinct behavioral patterns emerged from the analysis: the "brown bear group" (moderate PA and SB, high SP, N = 176, 53.5%), the "cheetah group" (high PA/MVPA, low SB, moderate SP, N = 102, 31%), and the "koala group" (low PA, high SB, lower SP, N = 51, 15.5%). After adjusting for potential confounding factors, it was found that compared with the "koala group", the "brown bear group" and the "cheetah group" exhibited higher levels of physical fitness, with the probability of improving their physical fitness rating being 3.69 times and 6.36 times that of the "koala group," respectively. This study highlights the significant impact of active and healthy activity patterns on the physical fitness of preschool children, providing a foundation for formulating personalized preventive and interventional approaches in early childhood. Show less
📄 PDF DOI: 10.1016/j.jesf.2026.200459
LPA
Xiaofang Chen, Yonghong Zheng, Shaowei Lin +3 more · 2026 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a well-established independent risk factor for cardiovascular disease. However, the long-term effects of Lp(a) on coronary plaque phenotype remain unclear. To explore the pot Show more
Lipoprotein(a) [Lp(a)] is a well-established independent risk factor for cardiovascular disease. However, the long-term effects of Lp(a) on coronary plaque phenotype remain unclear. To explore the potential association between Lp(a) levels and coronary plaque volume, composition, and progression using coronary computed tomography angiography (CCTA). Patients with available data for Lp(a) and underwent baseline CCTA examinations between January 2009 to December 2015 and subsequently underwent a follow-up coronary CTA were retrospectively enrolled. Quantitative CCTA analyses measured plaque length, total plaque volume and composition volume. Patients were categorized into an elevated Lp(a) group (≥30 mg/dL) and a normal Lp(a) group (<30 mg/dL). The association between Lp(a) and baseline plaque characteristic and progression were investigated in linear mixed-effects models adjusted for clinical factors. Subgroup analyses were also conducted. Among 453 patients (mean age 64.7 years, 77.7% male) with a median follow-up of 6.15 years. elevated Lp(a) was linked to higher baseline plaque burden (all Elevated Lp (a) level was associated with high coronary artery plaque burden at baseline and rapid progression of LAP at follow-up. Lp(a) may serve as a significant residual risk factor in seemingly "low-risk" populations. Show less
📄 PDF DOI: 10.3389/fcvm.2026.1699503
LPA
Xueyan Wang, Yingying Wang, Xinyue Chen +4 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Digital literacy has become a core competency for nursing professionals, enabling them to adapt to modern healthcare environments and engage effectively with emerging technologies. It is closely linke Show more
Digital literacy has become a core competency for nursing professionals, enabling them to adapt to modern healthcare environments and engage effectively with emerging technologies. It is closely linked to innovative behavior, which is essential for problem solving and advancing nursing practice. Despite its importance, limited research has examined differences in digital literacy among undergraduate nursing students and how these differences influence innovation. A cross-sectional study was conducted using a convenience sample of 450 undergraduate nursing students from four universities in Anhui Province, China. Participants completed a general information questionnaire, the Undergraduate Digital Literacy Scale, and the Innovative Behavior Scale. Latent profile analysis (LPA) was employed to classify students into distinct digital literacy profiles, while logistic regression and one-way ANOVA were used to explore factors influencing profile membership and the relationship between digital literacy and innovative behavior. Three latent profiles were identified: a "Low Digital Literacy" group (34.1%), a "Moderate Digital Literacy" group (15.9%), and a "High Digital Literacy" group (50.0%). Significant differences were observed across profiles in relation to gender, age, academic year, and frequency of artificial intelligence (AI) use in the past 6 months. Importantly, students with higher digital literacy consistently exhibited stronger innovative behavior ( Digital literacy among undergraduate nursing students is heterogeneous and shaped by demographic and experiential factors. Targeted educational interventions tailored to distinct literacy profiles are needed to bridge gaps, promote equity, and strengthen innovation. By integrating AI and advanced digital tools into nursing curricula, educators can enhance students' competencies and better prepare them to thrive in an increasingly digital and intelligent healthcare landscape. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1717234
LPA
Weiwei Xiang, Hua Ke, Xiaojia Song +10 more · 2026 · BMC women's health · BioMed Central · added 2026-04-24
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This stu Show more
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This study employed a cross-sectional design and was conducted from January to April 2024 in Wuhan, China. Participants were FSWs recruited through snowball sampling from entertainment venues, including hotels, restaurants, nightclubs, karaoke bars and dance halls. Data were collected via structured questionnaires covering sociodemographic information, work experience, psychological stress, health status, sleep quality and circadian rhythms. Latent profile analysis (LPA) was employed to identify health characteristic profiles among FSWs, and multivariate logistic regression was used to examine the associations between these profiles and sleep quality. Among the 1,036 FSWs surveyed, 45.1% had poor sleep quality. LPA classified FSWs’ health characteristics into three profiles: the high overall functioning group, the lower physical–emotional functioning group and the lower psychosocial functioning group. Multivariate logistic regression analysis showed that FSWs in the lower physical–emotional functioning group had higher odds of poor sleep quality (OR = 2.184) compared with those in the high overall functioning group. FSWs in the lower psychosocial functioning group had substantially higher odds of poor sleep quality (OR = 7.755) than that in the high overall functioning group. FSWs demonstrate substantial heterogeneity in health characteristics and exhibit lower overall sleep quality compared with the general population. Psychological and physiological factors are major influencing factors for their sleep quality, suggesting the importance of prioritising mental and physical health in this population. Show less
📄 PDF DOI: 10.1186/s12905-026-04346-w
LPA