👤 Jinman Li

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Also published as: A Li, Ai-Jun Li, Ai-Qin Li, Ailing Li, Aimin Li, Aixin Li, Alexander H Li, Alexander Li, Amy Li, An-Qi Li, AnHai Li, Anan Li, Andrew C Li, Ang Li, Anna Fen-Yau Li, Annie Li, Anqi Li, Anyao Li, Ao Li, Aowen Li, Aoxi Li, Audrey Li, Bai-Qiang Li, Baichuan Li, Baiqiang Li, Baixing Li, Baizhou Li, Bang-Yan Li, Bao Li, Bao-Shan Li, Baoguang Li, Baoguo Li, Baohong Li, Baohua Li, Baolin Li, Baoqi Li, Baoqing Li, Baosheng Li, Baoting Li, Bei Li, Bei-Bei Li, Beibei Li, Beixu Li, Ben Li, Ben-Shang Li, Benyi Li, Biao Li, Bichun Li, Bin Li, Bin-Kui Li, Binbin Li, Bing Li, Bing-Heng Li, Bing-Hui Li, Bing-Mei Li, Bingbing Li, Binghu Li, Binghua Li, Bingjie Li, Bingjue Li, Bingkun Li, Binglan Li, Bingong Li, Bingshan Li, Bingsheng Li, Bingsong Li, Bingxin Li, Binjun Li, Binkui Li, Binru Li, Binxing Li, Biyu Li, Bizhi Li, Bo Li, BoWen Li, Bohao Li, Bohua Li, Bolun Li, Boru Li, Botao Li, Boxuan Li, Boya Li, Boyang Li, Bugao Li, C H Li, C Li, C X Li, C Y Li, Caesar Z Li, Cai Li, Cai-Hong Li, Caihong Li, Caili Li, Caixia Li, Caiyu Li, Caiyun Li, Can Li, Cang Li, Caolong Li, Chang Li, Chang-Da Li, Chang-Ping Li, Chang-Sheng Li, Chang-Yan Li, Chang-hai Li, Changcheng Li, Changgui Li, Changhong Li, Changhui Li, Changjiang Li, Changkai Li, Changqing Li, Changwei Li, Changxian Li, Changyan Li, Changyu Li, Changzheng Li, Chanjuan Li, Chanyuan Li, Chao Bo Li, Chao Li, Chaochen Li, Chaojie Li, Chaonan Li, Chaoqian Li, Chaowei Li, Chaoying Li, Chen Li, Chen-Chen Li, Chen-Lu Li, Chen-Xi Li, Chenfeng Li, Cheng Li, Cheng-Lin Li, Cheng-Tian Li, Cheng-Wei Li, Chengbin Li, Chengcheng Li, Chenghao Li, Chenghong Li, Chengjian Li, Chengjun Li, Chenglan Li, Chenglong Li, Chengnan Li, Chengping Li, Chengqian Li, Chengquan Li, Chengsi Li, Chenguang Li, Chengwen Li, Chengxin Li, Chengyun Li, Chenhao Li, Chenjie Li, Chenli Li, Chenlin Li, Chenlong Li, Chenlu Li, Chenmeng Li, Chenrui Li, Chensheng Li, Chenwen Li, Chenxi Li, Chenxiao Li, Chenxin Li, Chenxuan Li, Chenyang Li, Chenyao Li, Chenyu Li, Cheung Li, Chi-Ming Li, Chi-Yuan Li, Chia Li, Chia-Yang Li, Chien-Feng Li, Chien-Hsiu Li, Chien-Te Li, Chih-Chi Li, Chitao Li, Chiyang Li, Chong Li, Chongyang Li, Chongyi Li, Chris Li, Chu-Qiao Li, Chuan F Li, Chuan Li, Chuan-Hai Li, Chuan-Yun Li, Chuanbao Li, Chuanfang Li, Chuang Li, Chuangpeng Li, Chuanning Li, Chuanyin Li, Chumei Li, Chun Li, Chun-Bo Li, Chun-Lai Li, Chun-Mei Li, Chun-Quan Li, Chun-Xiao Li, Chun-Xu Li, Chung-Hao Li, Chung-I Li, Chunhong Li, Chunhui Li, Chunjie Li, Chunjun Li, Chunlan Li, Chunlian Li, Chunliang Li, Chunlin Li, Chunmei Li, Chunmiao Li, Chunqing Li, Chunqiong Li, Chunshan Li, Chunsheng Li, Chunting Li, Chunxia Li, Chunxiao Li, Chunxing Li, Chunxue Li, Chunya Li, Chunyan Li, Chunyi Li, Chunying Li, Chunyu Li, Chunzhu Li, Chuzhong Li, Cien Li, Cong Li, Congcong Li, Congfa Li, Conghui Li, Congjiao Li, Conglin Li, Congxin Li, Congye Li, Cui Li, Cui-lan Li, Cuicui Li, Cuiguang Li, Cuilan Li, Cuiling Li, Cun Li, Cunxi Li, Cyril Li, D C Li, Da Li, Da-Hong Li, Da-Jin Li, Da-Lei Li, Da-wei Li, DaZhuang Li, Dacheng Li, Dai Li, Daiyue Li, Dalei Li, Dali Li, Dalin Li, Dan C Li, Dan Li, Dan-Dan Li, Dan-Ni Li, Dandan Li, Daniel Tian Li, Danjie Li, Danni Li, Danxi Li, Danyang Li, Daoyuan Li, Dapei Li, Dawei Li, Dayong Li, Dazhi Li, De-Jun Li, De-Tao Li, Dechao Li, Defa Li, Defeng Li, Defu Li, Dehai Li, Deheng Li, Dehua Li, Dejun Li, Demin Li, Deming Li, Dengfeng Li, Dengke Li, Dengxiong Li, Deqiang Li, Desen Li, Desheng Li, Dexiong Li, Deyu Li, Dezhi Li, Di Li, Di-Jie Li, Dianjie Li, Dijie Li, Ding Li, Ding Yang Li, Ding-Biao Li, Ding-Jian Li, Dingchen Li, Dingshan Li, Diyan Li, Dong Li, Dong Sheng Li, Dong-Jie Li, Dong-Ling Li, Dong-Run Li, Dong-Yun Li, Dong-fei Li, Dongbiao Li, Dongdong Li, Dongfang Li, Dongfeng Li, Donghe Li, Donghua Li, Dongliang Li, Dongmei Li, Dongmin Li, Dongnan Li, Dongtao Li, Dongyang Li, Dongye Li, Duan Li, Duanbin Li, Duanxiang Li, Dujuan Li, Duo Li, Duoyun Li, Ellen Li, En Li, En-Min Li, Enhao Li, Enhong Li, Enxiao Li, F Li, Fa-Hong Li, Fa-Hui Li, Fadi Li, Fan Li, Fang Li, Fangqi Li, Fangyan Li, Fangyong Li, Fangyuan Li, Fangzhou Li, Fei Li, Fei-Lin Li, Fei-feng Li, Feifei Li, Feilong Li, Fen Li, Feng Li, Feng-Feng Li, Fengfeng Li, Fengjuan Li, Fengli Li, Fengqi Li, Fengqiao Li, Fengqing Li, Fengxia Li, Fengxiang Li, Fengyi Li, Fengyuan Li, Fu-Rong Li, Fugen Li, Fuhai Li, Fujun Li, Fulun Li, Fuping Li, Fusheng Li, Fuyu Li, Fuyuan Li, G Li, G-P Li, Gaijie Li, Gaizhen Li, Gaizhi Li, Gan Li, Gang Li, Ganggang Li, Gao-Fei Li, Gaoyuan Li, Ge Li, Gen Li, Gen-Lin Li, Gerard Li, Gong-Hua Li, Gongda Li, Guanbin Li, Guandu Li, Guang Li, Guang Y Li, Guang-Li Li, Guang-Xi Li, Guangda Li, Guangdi Li, Guanghua Li, Guanghui Li, Guangjin Li, Guangli Li, Guanglu Li, Guanglve Li, Guangming Li, Guangping Li, Guangpu Li, Guangqiang Li, Guangquan Li, Guangwen Li, Guangxi Li, Guangxiao Li, Guangyan Li, Guangzhao Li, Guangzhen Li, Guannan Li, Guanqiao Li, Guanyu Li, Gui Lin Li, Gui-Bo Li, Gui-Hua Li, Gui-Rong Li, Gui-xing Li, Guigang Li, Guihua Li, Guilan Li, Guisen Li, Guixia Li, Guixin Li, Guiyang Li, Guiying Li, Guiyuan Li, Guo Li, Guo-Chun Li, Guo-Jian Li, Guo-Li Li, Guo-Ping Li, Guo-Qiang Li, Guobin Li, Guoge Li, Guohong Li, Guohua Li, Guohui Li, Guojin Li, Guojun Li, Guoli Li, Guoping Li, Guoqin Li, Guoqing Li, Guowei Li, Guoxi Li, Guoxiang Li, Guoxing Li, Guoyan Li, Guoyin Li, H J Li, H Li, H-F Li, H-H Li, H-J Li, Hai Li, Hai-Yun Li, Haibin Li, Haibo Li, Haifeng Li, Haihong Li, Haihua Li, Haijun Li, Hailong Li, Haimin Li, Haiming Li, Hainan Li, Haipeng Li, Hairong Li, Haitao Li, Haitong Li, Haixia Li, Haiyan Li, Haiyang Li, Haiying Li, Haiyu Li, Han Li, Han-Bing Li, Han-Bo Li, Han-Ni Li, Han-Ru Li, Han-Wei Li, Hanbin Li, Hanbing Li, Hanbo Li, Handong Li, Hang Li, Hangwen Li, Hanjun Li, Hankun Li, Hanlu Li, Hanmei Li, Hanqi Li, Hanqin Li, Hansen Li, Hanting Li, Hanxiao Li, Hanxue Li, Hao Li, Hao-Fei Li, Haojing Li, Haolong Li, Haomiao Li, Haoqi Li, Haoran Li, Haotong Li, Haoxian Li, Haoyu Li, Haying Li, He Li, He-Zhen Li, Hecheng Li, Hegen Li, Hehua Li, Heng Li, Heng-Zhen Li, Hengguo Li, Hengtong Li, Hengyu Li, Hening Li, Hewei Li, Hexin Li, Heying Li, Hong Li, Hong-Chun Li, Hong-Lan Li, Hong-Lian Li, Hong-Mei Li, Hong-Tao Li, Hong-Wen Li, Hong-Yan Li, Hong-Yu Li, Hong-Zheng Li, Hongbo Li, Hongchang Li, Hongde Li, Honggang Li, Hongguo Li, Honghua Li, Honghui Li, Hongjia Li, Hongjiang Li, Hongjuan Li, Honglei Li, Hongli Li, Honglian Li, Hongliang Li, Honglin Li, Hongling Li, Honglong Li, Hongmei Li, Hongmin Li, Hongming Li, Hongqin Li, Hongquan Li, Hongru Li, Hongsen Li, Hongwei Li, Hongxia Li, Hongxin Li, Hongxing Li, Hongxue Li, Hongyan Li, Hongye Li, Hongyi Li, Hongyu Li, Hongyun Li, Hongzhe K Li, Hongzheng Li, Hongzhi Li, Hsiao-Fen Li, Hsiao-Hui Li, Hsin-Hua Li, Hsin-Yun Li, Hu Li, Hua Li, Hua-Zhong Li, Huabin Li, Huafang Li, Huafu Li, Huaixing Li, Huaiyuan Li, Hualian Li, Hualing Li, Huamao Li, Huan Li, Huanan Li, Huang Li, Huangbao Li, Huangyuan Li, Huanhuan Li, Huanjun Li, Huanqing Li, Huanqiu Li, Huaping Li, Huashun Li, Huawei Li, Huayao Li, Huayin Li, Huaying Li, Hui Li, Hui-Jun Li, Hui-Long Li, Hui-Ping Li, Huibo Li, Huifang Li, Huifeng Li, Huihuang Li, Huihui Li, Huijie Li, Huijuan Li, Huijun Li, Huilan Li, Huili Li, Huiliang Li, Huilin Li, Huilong Li, Huimin Li, Huiping Li, Huiqin Li, Huiqing Li, Huiqiong Li, Huiting Li, Huixia Li, Huixue Li, Huiying Li, Huiyou Li, Huiyuan Li, Huizi Li, Hujie Li, Hulun Li, Hung Li, Hung-Yuan Li, Ivan Li, J Li, J T Li, Jason Li, Jen-Ming Li, Jenny J Li, Ji Li, Ji Xia Li, Ji-Cheng Li, Ji-Feng Li, Ji-Liang Li, Ji-Lin Li, Ji-Min Li, Jia Li, Jia Li Li, Jia-Da Li, Jia-Huan Li, Jia-Peng Li, Jia-Ru Li, Jia-Xin Li, Jiabei Li, Jiachen Li, Jiacheng Li, Jiafang Li, Jiafei Li, Jiahao Li, Jiahui Li, Jiajia Li, Jiajie Li, Jiajing Li, Jiajun Li, Jiajv Li, Jiali Li, Jialin Li, Jialing Li, Jialun Li, Jiaming Li, Jian Li, Jian'an Li, Jian-Jun Li, Jian-Mei Li, Jian-Qiang Li, Jian-Shuang Li, Jianan Li, Jianang Li, Jianbin Li, Jianbo Li, Jianchun Li, Jiandong Li, Jianfang Li, Jianfeng Li, Jiang Li, Jiangan Li, Jiangbo Li, Jiangchao Li, Jiangfeng Li, Jianglin Li, Jianglong Li, Jiangtao Li, Jiangui Li, Jianguo Li, Jiangxia Li, Jiangya Li, Jianhai Li, Jianhua Li, Jiani Li, Jianing Li, Jianliang Li, Jianlin Li, Jianmin Li, Jiannan Li, Jianping Li, Jianrong Li, Jianrui Li, Jiansheng Li, Jianshuang Li, Jianwei Li, Jianxin Li, Jianxiong Li, Jianye Li, Jianyi Li, Jianyong Li, Jianyu Li, Jianzhong Li, Jiao Li, Jiao-Jiao Li, Jiaomei Li, Jiaping Li, Jiaqi Li, Jiawei Li, Jiaxi Li, Jiaxin Li, Jiaxuan Li, Jiayan Li, Jiayang Li, Jiayi Li, Jiaying Li, Jiayu Li, Jiayuan Li, Jiazhou Li, Jicheng Li, Jie Li, Jie-Pin Li, Jie-Shou Li, Jiehan Li, Jiejia Li, Jiejie Li, Jiejing Li, Jieming Li, Jiequn Li, Jieshou Li, Jiexi Li, Jiexin Li, Jiezhen Li, Jifang Li, Jihua Li, Jin Li, Jin-Jiang Li, Jin-Liang Li, Jin-Long Li, Jin-Mei Li, Jin-Ping Li, Jin-Qiu Li, Jin-Wei Li, Jin-Xiu Li, Jinchen Li, Jinfang Li, Jinfeng Li, Jing Li, Jing-Jing Li, Jing-Ming Li, Jing-Yao Li, Jing-Yi Li, Jing-gao Li, Jingcheng Li, Jingchun Li, Jingfeng Li, Jinghao Li, Jinghui Li, Jingjing Li, Jingke Li, Jinglin Li, Jingmei Li, Jingming Li, Jingping Li, Jingqi Li, Jingshang Li, Jingshu Li, Jingtong Li, Jingui Li, Jingwen Li, Jingxia Li, Jingxiang Li, Jingxin Li, Jingya Li, Jingyi Li, Jingyong Li, Jingyu Li, Jingyun Li, Jinhua Li, Jinhui Li, Jinjie Li, Jinku Li, Jinlan Li, Jinliang Li, Jinlin Li, Jinming Li, Jinping Li, Jinsong Li, Jinwei Li, Jinxia Li, Jinxin Li, Jinzhi Li, Jiong Li, Jiong-Ming Li, Jipeng Li, Jiqing Li, Jisen Li, Jisheng Li, Jiuke Li, Jiuyi Li, Jiwei Li, Jiwen Li, Jixi Li, Jixuan Li, Jiyang Li, Jiyuan Li, John Zhong Li, Jonathan Z Li, Joyce Li, Ju-Rong Li, Juan Li, Juan-Juan Li, Juanjuan Li, Juanling Li, Juanni Li, Jufang Li, Julia Li, Jun Li, Jun Z Li, Jun-Cheng Li, Jun-Jie Li, Jun-Ling Li, Jun-Ru Li, Jun-Yan Li, Jun-Ying Li, JunBo Li, Junfeng Li, Junhong Li, Junhui Li, Junjie Li, Junjun Li, Junming Li, Junping Li, Junqin Li, Junru Li, Junsheng Li, Juntong Li, Junxian Li, Junxin Li, Junxu Li, Junya Li, Junyi Li, Junying Li, Justin Li, Jutang Li, Juxue Li, K-L Li, Ka Li, Ka Wan Li, Kai Li, Kai-Wen Li, Kaibin Li, Kaibo Li, Kaifeng Li, Kailong Li, Kaimi Li, Kainan Li, Kaiwei Li, Kaixin Li, Kaiyi Li, Kaiyuan Li, Kang Li, Kangli Li, Kangyuan Li, Karen Li, Kathy H Li, Kawah Li, Ke Li, KeZhong Li, Keanning Li, Kecheng Li, Kechun Li, Keguo Li, Kejuan Li, Keke Li, Kening Li, Kenli Li, Kenneth Kai Wang Li, Keqing Li, Keshen Li, Keying Li, Keyuan Li, Kezhen Li, Kongdong Li, Kuan Li, Kui Li, Kuiliang Li, Kun Li, Kun-Peng Li, Kun-Ping Li, Kun-Xin Li, Kunlin Li, Kunlong Li, Kunlun Li, Kunpeng Li, L I Li, L K Li, L Li, L P Li, L-Y Li, Lai K Li, Laiqing Li, Lamei Li, Lan Li, Lan-Juan Li, Lan-Lan Li, Lanfang Li, Lang Li, Lanjuan Li, Lanlan Li, Lanzhou Li, Le Li, Le-Le Li, Le-Ying Li, Lei Li, Leilei Li, Leipeng Li, Letai Li, Leyao Li, Li Li, Li-Min Li, Li-Na Li, Lian Li, Lianbing Li, Liang Li, Liangdong Li, Liangji Li, Liangkui Li, Liangqian Li, Lianhong Li, Lianjian Li, Lianyong Li, Liao-Yuan Li, Lieyou Li, Liguo Li, Lihong Li, Lihua Li, Lijia Li, Lijuan Li, Lijun Li, Lili Li, Liliang Li, Liling Li, Liming Li, Lin Li, Lin-Feng Li, Linchuan Li, Linfeng Li, Ling Li, Ling-Jie Li, Ling-Ling Li, Ling-Zhi Li, Lingjiang Li, Lingjie Li, Lingjun Li, Lingling Li, Lingxi Li, Lingyan Li, Lingyi Li, Lingzhi Li, Linhong Li, Linke Li, Linlin Li, Linqi Li, Linqing Li, Linsheng Li, Linting Li, Linxin Li, Linyan Li, Linying Li, Lipeng Li, Liping Li, Liqin Li, Liqun Li, Lirong Li, Lisha Li, Litao Li, Liuzheng Li, Liwei Li, Lixi Li, Lixia Li, Lixiang Li, Liyan Li, Long Li, Long Shan Li, Long-Yan Li, Longhui Li, Longxuan Li, Longyu Li, Lu Li, Lu-Yun Li, Lucia M Li, Lucy Li, Luhan Li, Lujiao Li, Lujie Li, Lulu Li, Luquan Li, Luxuan Li, Luyao Li, Luying Li, M D Li, M Li, M V Li, M-J Li, Man Li, Man-Xiang Li, Man-Zhi Li, Mangmang Li, Manjiang Li, Manna Li, Manru Li, Manxia Li, Mao Li, Maogui Li, Maolin Li, Maoquan Li, Maosheng Li, Marilyn Li, Mei Li, Mei-Lan Li, Mei-Ya Li, Mei-Zhen Li, Meifang Li, Meifen Li, Meijia Li, Meilan Li, Meiqing Li, Meitao Li, Meiting Li, Meiyan Li, Meiying Li, Meiyue Li, Meizi Li, Melody M H Li, Meng Li, Meng-Hua Li, Meng-Jun Li, Meng-Meng Li, Meng-Miao Li, Meng-Yang Li, Meng-Yao Li, Meng-Yue Li, MengGe Li, Mengfan Li, Menghua Li, Mengjiao Li, Mengjuan Li, Mengling Li, Menglu Li, Mengmeng Li, Mengqing Li, Mengqiu Li, Mengsen Li, Mengshi Li, Mengxi Li, Mengxia Li, Mengxuan Li, Mengyang Li, Mengyao Li, Mengying Li, Mengyuan Li, Mengyun Li, Mengze Li, Mi Li, Mian Li, Miao Li, Miao X Li, Miaoxin Li, Michelle Li, Mimi Li, Min Li, Min-Dian Li, Min-Rui Li, Min-jun Li, Minerva X Li, Ming D Li, Ming Li, Ming V Li, Ming Xing Li, Ming Zhou Li, Ming-Han Li, Ming-Hao Li, Ming-Jiang Li, Ming-Kai Li, Ming-Qing Li, Ming-Wei Li, Ming-Xing Li, Ming-Yang Li, Mingdan Li, Mingfang Li, Mingfei Li, Minghao Li, Minghua Li, Minghui Li, Mingjiang Li, Mingjie Li, Mingjun Li, Mingke Li, Mingkun Li, Mingli Li, Minglong Li, Minglun Li, Mingna Li, Mingqiang Li, Mingquan Li, Mingrui Li, Mingwei Li, Mingxi Li, Mingxia Li, Mingxing Li, Mingxu Li, Mingxuan Li, Mingyang Li, Mingyao Li, Mingyue Li, Mingzhe Li, Mingzhou Li, Minhui Li, Minle Li, Minmin Li, Minqi Li, Minyue Li, Minze Li, Minzhe Li, Miyang Li, Mo Li, Mohan Li, Monica M Li, Moyi Li, Mufan Li, Mulin Jun Li, Muzi Li, N Li, Na Li, Naishi Li, Nan Li, Nan-Nan Li, Nana Li, Nanjun Li, Nanlong Li, Nanxing Li, Nanzhen Li, Ni Li, Nianfu Li, Nianyu Li, Nien Li, Nien-Chen Li, Nien-Chi Li, Ning Li, Ningyan Li, Ningyang Li, Niu Li, Nuomin Li, O Li, P H Li, P Li, Pan Li, Panlong Li, Panyuan Li, Pei Li, Pei-Lin Li, Pei-Qin Li, Pei-Shan Li, Pei-Ying Li, Pei-Zhi Li, PeiQi Li, Peibo Li, Peifen Li, Peifeng Li, Peihong Li, Peihua Li, Peilin Li, Peilong Li, Peining Li, Peipei Li, Peiqin Li, Peiran Li, Peiwu Li, Peixin Li, Peiyu Li, Peiyuan Li, Peiyun Li, Peng Li, Peng Peng Li, Peng-li Li, Pengcui Li, Penghui Li, Pengjie Li, Pengju Li, Pengsong Li, Pengyang Li, Pengyu Li, Pengyun Li, Pik Yi Li, Pilong Li, Pindong Li, Ping Li, Ping'an Li, Pinghua Li, Pingping Li, Pu Li, Pu-Yu Li, Q Li, Qi Li, Qi-Fu Li, Qi-Jing Li, Qian Li, Qian-Qian Li, Qiang Li, Qiang-Ming Li, Qiankun Li, Qianqian Li, Qiao Li, Qiao-Xin Li, Qiaolian Li, Qiaoqiao Li, Qibing Li, Qifang Li, Qihang Li, Qihua Li, Qiji Li, Qijun Li, Qilan Li, Qilong Li, Qin Li, Qiner Li, Qing Li, Qing Run Li, Qing-Chang Li, Qing-Fang Li, Qing-Min Li, Qing-Wei Li, Qingchao Li, Qingfang Li, Qingfeng Li, Qinggang Li, Qinghe Li, Qinghong Li, Qinghua Li, Qingjie Li, Qinglan Li, Qingli Li, Qinglin Li, Qingling Li, Qingqin S Li, Qingrun Li, Qingshang Li, Qingsheng Li, Qingxian Li, Qingyang Li, Qingyu Li, Qingyuan Li, Qingyun Li, Qinqin Li, Qinrui Li, Qintong Li, Qiong Li, Qionghua Li, Qipei Li, Qiqiong Li, Qiu Li, Qiufeng Li, Qiuhong Li, Qiusheng Li, Qiuxuan Li, Qiuya Li, Qiuyan Li, Qiwei Li, Qiyong Li, Qizhai Li, Quan Li, Quan-Zhong Li, Quanpeng Li, Quanshun Li, Quanzhang Li, Qun Li, R H L Li, R Li, Ran Li, Ranchang Li, Ranran Li, Ranwei Li, Ren Li, Ren-Ke Li, Rena Li, Roger Li, Ronald Li, Rong Li, Rong-Bing Li, Ronggui Li, Rongkai Li, Rongling Li, Rongqing Li, Rongsong Li, Rongxia Li, Rongyao Li, Rosa J W Li, Ru Li, Ru-Hao Li, Rui Li, Rui-Fang Li, Rui-Han Li, Rui-Jún Eveline Li, Ruibing Li, Ruidong Li, Ruifang Li, Ruihuan Li, Ruijia Li, Ruijin Li, Ruikai Li, Ruitong Li, Ruiwen Li, Ruixi Li, Ruixia Li, Ruixue Li, Ruiyang Li, Rujia Li, Rulin Li, Rumei Li, Runbing Li, Runwen Li, Runzhao Li, Runzhen Li, Runzhi Li, Ruobing Li, Ruolin Li, Ruonan Li, Ruotai Li, Ruotian Li, Ruotong Li, Ruyi Li, Ruyue Li, S A Li, S E Li, S L Li, S Li, S S Li, S-C Li, Sai Li, Saijuan Li, Sainan Li, San-Feng Li, Sanqiang Li, Senlin Li, Senmao Li, Sha Li, Sha-Sha Li, Shan Li, Shan-Shan Li, Shangjia Li, Shanglai Li, Shangming Li, Shanhang Li, Shanpeng Li, Shanshan Li, Shanyi Li, Shao-Dan Li, Shaobin Li, Shaodan Li, Shaofei Li, Shaoguang Li, Shaojian Li, Shaojing Li, Shaoliang Li, Shaomin Li, Shaoqi Li, Shaoyong Li, Shasha Li, Shawn S C Li, Shawn Shun-Cheng Li, Shen Li, Sheng Li, Sheng-Fu Li, Sheng-Jie Li, Sheng-Qing Li, Sheng-Tien Li, Shengbiao Li, Shengbin Li, Shengchao A Li, Shenghao Li, Shengjie Li, Shengli Li, Shengliang Li, Shengsheng Li, Shengwen Li, Shengxian Li, Shengxu Li, Shengze Li, Sherly X Li, Shi Li, Shi-Fang Li, Shi-Guang Li, Shi-Hong Li, Shi-Ying Li, Shibao Li, Shibo Li, Shichao Li, Shigang Li, Shihao Li, Shiheng Li, Shihong Li, Shijie Li, Shijun Li, Shikang Li, Shilan Li, Shili Li, Shiliang Li, Shilin Li, Shilun Li, Shiqi Li, Shiquan Li, Shisheng Li, Shishi Li, Shitao Li, Shiya Li, Shiyan Li, Shiyang Li, Shiyi Li, Shiying Li, Shiyu Li, Shiyue Li, Shiyun Li, Shu Li, Shu-Fang Li, Shu-Fen Li, Shu-Feng Li, Shu-Hong Li, Shu-Qi Li, Shu-Xin Li, Shuai Li, Shuaicheng Li, Shuang Li, Shuang-Ling Li, Shuangding Li, Shuangfei Li, Shuanglong Li, Shuangmei Li, Shuangshuang Li, Shuangxiu Li, Shubo Li, Shude Li, Shufen Li, Shugang Li, Shuguang Li, Shuhao Li, Shuhua Li, Shuhui Li, Shujiao Li, Shujie Li, Shujin Li, Shujing Li, Shulin Li, Shun Li, Shunhua Li, Shunle Li, Shunqin Li, Shunqing Li, Shunwang Li, Shuo Li, Shupeng Li, Shuqiang Li, Shuwei Li, Shuwen Li, Shuying Li, Shuyu D Li, Shuyu Dan Li, Shuyuan Li, Shuyue Li, Si Li, Si-Wei Li, Si-Xing Li, Si-Ying Li, Si-Yuan Li, Sibing Li, Sichen Li, Sichong Li, Side Li, Siguang Li, Sijie Li, Simin Li, Siming Li, Sin-Lun Li, Siqi Li, Sitao Li, Siting Li, Siwen Li, Siyi Li, Siyu Li, Siyue Li, Song Li, Song-Chao Li, Songhan Li, Songlin Li, Songtao Li, Songyu Li, Songyun Li, Stephen Li, Su Li, SuYun Li, Suchun Li, Suheng Li, Suhong Li, Suiyan Li, Sujing Li, Suk-Yee Li, Sumei Li, Sunan Li, Sung-Chou Li, Supeng Li, Suping Li, Suran Li, Suwei Li, Suwen Li, Suyan Li, T Li, Taibo Li, Taiwen Li, Taixu Li, Tao Li, Taoyingnan Li, Teng Li, Tengyan Li, Thomas Li, Tian Li, Tian-Yi Li, Tian-chang Li, Tian-wang Li, Tianchang Li, Tiandong Li, Tianfeng Li, Tiange Li, Tianjiao Li, Tianjun Li, Tianming Li, Tiansen Li, Tiantian Li, Tianxiang Li, Tianyao Li, Tianye Li, Tianyi Li, Tianyou Li, Tie Li, Tiegang Li, Tiehua Li, Tiewei Li, Timmy Li, Ting Li, Tingguang Li, Tinghao Li, Tinghua Li, Tingsong Li, Tingting Li, Tong Li, Tong-Ruei Li, Tongyao Li, Tongzheng Li, Tsai-Kun Li, Tuojian Li, Tuoping Li, Vivian Li, Vivian S W Li, W H Li, W J Li, W Li, W W Li, W Y Li, W-B Li, Wan Jie Li, Wan Li, Wan-Hong Li, Wan-Shan Li, Wan-Xin Li, Wang Li, Wanling Li, Wanni Li, Wanqian Li, Wanru Li, Wanshi Li, Wanshun Li, Wanting Li, Wanwan Li, Wanxin Li, Wanyan Li, Wanyi Li, Wei Li, Wei-Bo Li, Wei-Dong Li, Wei-Jun Li, Wei-Li Li, Wei-Ming Li, Wei-Na Li, Wei-Ping Li, Wei-Qin Li, Wei-Yang Li, Weidong Li, Weifeng Li, Weiguang Li, Weiguo Li, Weihai Li, Weiheng Li, Weihua Li, Weijian Li, Weijie Li, Weijun Li, Weike Li, Weiling Li, Weimin Li, Weina Li, Weining Li, Weiping Li, Weiqin Li, Weirong Li, Weisong Li, Weiyang Li, Weiye Li, Weiyong Li, Weizu Li, Wen Lan Li, Wen Li, Wen-Chao Li, Wen-Jie Li, Wen-Ting Li, Wen-Wen Li, Wen-Xi Li, Wen-Xing Li, Wen-Ya Li, Wen-Ying Li, Wen-juan Li, Wenbo Li, Wenchao Li, Wende Li, Wendeng Li, Wenfang Li, Wenfeng Li, Wenge Li, Wenguo Li, Wenhao Li, Wenhong Li, Wenhua Li, Wenhui Li, Wenjia Li, Wenjian Li, Wenjie Li, Wenjing Li, Wenjuan Li, Wenjun Li, Wenke Li, Wenlei Li, Wenli Li, Wenlong Li, Wenming Li, Wenqi Li, Wenqiang Li, Wenqing Li, Wenqun Li, Wenrui Li, Wensheng Li, Wentao Li, Wenwen Li, Wenxi Li, Wenxia Li, Wenxiang Li, Wenxin Li, Wenxiu Li, Wenxue Li, Wenyan Li, Wenyang Li, Wenyi Li, Wenying Li, Wenyong Li, Wenyu Li, Wenzhe Li, Wenzhuo Li, Wu-Jun Li, Wuguo Li, Wulan Li, Wuyan Li, X B Li, X L Li, X Li, X Y Li, X-H Li, X-L Li, Xi Li, Xi-Hai Li, Xi-Xi Li, Xia Li, Xian Li, Xiancheng Li, Xiang Li, Xiang-Dong Li, Xiang-Jun Li, Xiang-Ping Li, Xiang-Yu Li, Xiangcheng Li, Xiangchun Li, Xiangdong Li, Xiangfei Li, Xiangjun Li, Xiangling Li, Xianglong Li, Xiangnan Li, Xiangpan Li, Xiangping Li, Xiangqi Li, Xiangrui Li, Xiangwei Li, Xiangyan Li, Xiangyang Li, Xiangyun Li, Xiangzhe Li, Xiankai Li, Xiankun Li, Xianlin Li, Xianlong Li, Xianlu Li, Xianlun Li, Xianrui Li, Xianyong Li, Xiao Li, Xiao-Cheng Li, Xiao-Dong Li, Xiao-Feng Li, Xiao-Gang Li, Xiao-Guang Li, Xiao-Hong Li, Xiao-Hui Li, Xiao-Jiao Li, Xiao-Jing Li, Xiao-Jun Li, Xiao-Kang Li, Xiao-Li Li, Xiao-Lin Li, Xiao-Long Li, Xiao-Min Li, Xiao-Na Li, Xiao-Qiang Li, Xiao-Qin Li, Xiao-Qiu Li, Xiao-Sa Li, Xiao-Tong Li, Xiao-Yao Li, Xiao-Yun Li, Xiao-kun Li, Xiao-mei Li, Xiao-xu Li, Xiao-yu Li, XiaoQiu Li, Xiaobai Li, Xiaobin Li, Xiaobing Li, Xiaobo Li, Xiaochen Li, Xiaochun Li, Xiaocun Li, Xiaodong Li, Xiaofang Li, Xiaofei Li, Xiaofeng Li, Xiaoguang Li, Xiaohan Li, Xiaoheng Li, Xiaohong Li, Xiaohu Li, Xiaohua Li, Xiaohuan Li, Xiaohui Li, Xiaojiao Li, Xiaojiaoyang Li, Xiaojing Li, Xiaoju Li, Xiaojuan Li, Xiaokun Li, Xiaolei Li, Xiaoli Li, Xiaolian Li, Xiaoliang Li, Xiaolin Li, Xiaoling Li, Xiaolong Li, Xiaoman Li, Xiaomei Li, Xiaomeng Li, Xiaomin Li, Xiaoming Li, Xiaona Li, Xiaonan Li, Xiaoning Li, Xiaopeng Li, Xiaoping Li, Xiaoqi Li, Xiaoqiang Li, Xiaoqin Li, Xiaoqing Li, Xiaoqiong Li, Xiaoquan Li, Xiaoran Li, Xiaorong Li, Xiaotian Li, Xiaoting Li, Xiaotong Li, Xiaowei Li, Xiaoxia Li, Xiaoxiao Li, Xiaoxiong Li, Xiaoxuan Li, Xiaoya Li, Xiaoyan Li, Xiaoyao Li, Xiaoyi Li, Xiaoying Li, Xiaoyong Li, Xiaoyu Li, Xiaoyuan Li, Xiaoyun Li, Xiaozhao Li, Xiaozhen Li, Xiaozheng Li, Xiatian Li, Xiawei Li, Xiaxia Li, Xiayu Li, Xidan Li, Xihao Li, Xihe Li, Xijing Li, Xikun Li, Xiliang Li, Ximei Li, Xin Li, Xin-Chang Li, Xin-Jian Li, Xin-Ping Li, Xin-Tao Li, Xin-Ya Li, Xin-Yu Li, Xin-Yue Li, Xin-Zhu Li, Xinbin Li, Xing Li, Xing-Wang Li, Xingchen Li, Xingcheng Li, Xingfang Li, Xinghuan Li, Xinghui Li, Xingli Li, Xinglong Li, Xingwang Li, Xingxing Li, Xingya Li, Xingye Li, Xingyu Li, Xingyuan Li, Xinhai Li, Xinhua Li, Xinhui Li, Xining Li, Xinjia Li, Xinjian Li, Xinke Li, Xinle Li, Xinli Li, Xinlin Li, Xinmei Li, Xinmiao Li, Xinmin Li, Xinming Li, Xinpeng Li, Xinping Li, Xinrong Li, Xinrui Li, Xinsheng Li, Xinwei Li, Xinxin Li, Xinxiu Li, Xinyan Li, Xinyang Li, Xinyao Li, Xinye Li, Xinyi Li, Xinyu Li, Xinyuan Li, Xinzhi Li, Xinzhong Li, Xiong Bing Li, Xiong Li, Xiongfeng Li, Xionghao Li, Xionghui Li, Xiu-Ling Li, Xiucui Li, Xiufeng Li, Xiujuan Li, Xiuli Li, Xiuling Li, Xiumei Li, Xiuqi Li, Xiurong Li, Xiushen Li, Xiushi Li, Xiuzhen Li, Xixi Li, Xiying Li, Xiyue Li, Xiyun Li, Xu Li, Xu-Bo Li, Xu-Wei Li, Xu-Zhao Li, Xuan Li, Xuan-Ling Li, Xuanfei Li, Xuanxuan Li, Xuanzheng Li, Xudong Li, Xue Cheng Li, Xue Li, Xue-Er Li, Xue-Fei Li, Xue-Hua Li, Xue-Lian Li, Xue-Min Li, Xue-Nan Li, Xue-Peng Li, Xue-Yan Li, Xue-Ying Li, Xue-jing Li, Xue-zhi Li, Xuebiao Li, Xueer Li, Xuefei Li, Xuefeng Li, Xuehua Li, Xuejie Li, Xuejun Li, Xuekun Li, Xuelian Li, Xuelin Li, Xueling Li, Xuemei Li, Xuemin Li, Xuening Li, Xuepeng Li, Xueqin Li, Xueren Li, Xueshan Li, Xuesong Li, Xueting Li, Xuewang Li, Xuewei Li, Xuewen Li, Xueyang Li, Xueyi Li, Xueying Li, Xuezhong Li, Xuhang Li, Xuhong Li, Xuhua Li, Xujun Li, Xun Li, Xunjia Li, Xuri Li, Xutong Li, Xuyi Li, Xuze Li, Y H Li, Y L Li, Y Li, Y M Li, Y X Li, Y-Y Li, Ya Li, Ya-Feng Li, Ya-Ge Li, Ya-Jun Li, Ya-Li Li, Ya-Pei Li, Ya-Qiang Li, Ya-Ting Li, Ya-Zhou Li, YaJie Li, Yadong Li, Yahui Li, Yajiao Li, Yajing Li, Yajuan Li, Yajun Li, Yakui Li, Yalan Li, Yali Li, Yalin Li, Yan Bing Li, Yan Li, Yan Ning Li, Yan-Chun Li, Yan-Guang Li, Yan-Hong Li, Yan-Hua Li, Yan-Li Li, Yan-Nan Li, Yan-Xue Li, Yan-Yan Li, Yan-Yu Li, Yanan Li, Yanbin Li, Yanbing Li, Yanbo Li, Yanchang Li, Yanchuan Li, Yanchun Li, Yandong Li, Yanfeng Li, Yang Li, Yangxue Li, Yangyang Li, Yanhui Li, Yani Li, Yanjiao Li, Yanjie Li, Yanjing Li, Yanjun Li, Yanli Li, Yanlin Li, Yanling Li, Yanlong Li, Yanmei Li, Yanmin Li, Yanming Li, Yanni Li, Yanping Li, Yanqing Li, Yansen Li, Yanshu Li, Yansong Li, Yantao Li, Yanwei Li, Yanwu Li, Yanxi Li, Yanxiang Li, Yanxin Li, Yanyan Li, Yanying Li, Yanze Li, Yanzhong Li, Yao Li, Yaobo Li, Yaochen Li, Yaodong Li, Yaofu Li, Yaojia Li, Yaokun Li, Yaoqi Li, Yaoyao Li, Yaqi Li, Yaqiang Li, Yaqiao Li, Yaqin Li, Yaqing Li, Yaqiong Li, Yarong Li, Yawei Li, Yaxi Li, Yaxian Li, Yaxiong Li, Yaxuan Li, Yaying Li, Yayu Li, Yazhou Li, Ye Li, Yehong Li, Yeshan Li, Yetian Li, Yi Li, Yi-Heng Li, Yi-Ling Li, Yi-Ning Li, Yi-Shuan J Li, Yi-Ting Li, Yi-Wen Li, Yi-Yang Li, Yi-Ying Li, Yi-Yun Li, YiPing Li, YiQing Li, Yibo Li, Yiche Li, Yicun Li, Yifan Li, Yifei Li, Yifeng Li, Yige Li, Yihan Li, Yihao Li, Yiheng Li, Yihong Li, Yijian Li, Yijie Li, Yijing Li, Yiju Li, Yikang Li, Yike Li, Yilang Li, Yiliang Li, Yilong Li, Yimei Li, Yimeng Li, Yiming Li, Yin Li, Yinan Li, Ying Li, Ying-Bo Li, Ying-Lan Li, Ying-Qin Li, Ying-Qing Li, Ying-na Li, Yinggao Li, Yinghao Li, Yinghua Li, Yinghui Li, Yingjian Li, Yingjie Li, Yingjun Li, Yinglin Li, Yingnan Li, Yingpu Li, Yingqin Li, Yingrui Li, Yingshuo Li, Yingxi Li, Yingxia Li, Yingyi Li, Yingying Li, Yinhao Li, Yining Li, Yinliang Li, Yinxiong Li, Yinyan Li, Yinzhen Li, Yipeng Li, Yiqiang Li, Yirun Li, Yitong Li, Yiwei Li, Yiwen Li, Yixi Li, Yixiang Li, Yixiao Li, Yixin Li, Yixing Li, Yixuan Li, Yixue Li, Yiyang Li, Yizhe Li, Yong Li, Yong-Jian Li, Yong-Jun Li, Yong-Liang Li, Yongchao Li, Yonghao Li, Yonghe Li, Yongjia Li, Yongjiang Li, Yongjin Li, Yongjing Li, Yongjun Li, Yongkai Li, Yongle Li, Yongli Li, Yongmei Li, Yongnan Li, Yongpeng Li, Yongping Li, Yongqi Li, Yongqiang Li, Yongqiu Li, Yongsen Li, Yongsheng Li, Yongting Li, Yongxiang Li, Yongxin Li, Yongxue Li, Yongze Li, Yongzhe Li, Yongzhen Li, Yongzheng Li, You Li, You Ran Li, You-Mei Li, Youchen Li, Youjun Li, Youming Li, Youran Li, Yousheng Li, Youwei Li, Yu Li, Yu-Cheng Li, Yu-Chia Li, Yu-Hang Li, Yu-Hao Li, Yu-He Li, Yu-Hui Li, Yu-I Li, Yu-Jin Li, Yu-Jui Li, Yu-Kun Li, Yu-Lin Li, Yu-Sheng Li, Yu-Xiang Li, Yu-Ye Li, Yu-Ying Li, Yu-quan Li, Yuan Hao Li, Yuan Li, Yuan-Hai Li, Yuan-Jing Li, Yuan-Tao Li, Yuan-Yuan Li, Yuan-hao Li, Yuanchang Li, Yuanchuang Li, Yuancong Li, Yuandong Li, Yuanfang Li, Yuanfei Li, Yuanhao Li, Yuanhe Li, Yuanheng Li, Yuanhong Li, Yuanhua Li, Yuanjing Li, Yuanmei Li, Yuanyou Li, Yuanyuan Li, Yuanze Li, Yubin Li, Yubo Li, Yuchan Li, Yuchao Li, Yucheng Li, Yuchuan Li, Yuchun Li, Yudong Li, Yue Li, Yue-Chun Li, Yue-Jia Li, Yue-Ming Li, Yue-Rui Li, Yue-Ting Li, Yue-Ying Li, YueQiang Li, Yuefei Li, Yuefeng Li, Yueguo Li, Yuehua Li, Yuemei Li, Yueping Li, Yueqi Li, Yueting Li, Yuezheng Li, Yufan Li, Yufen Li, Yufeng Li, Yuguang Li, Yuhan Li, Yuhang Li, Yuhong Li, Yuhua Li, Yuhuang Li, Yuhui Li, Yujie Li, Yujun Li, Yukun Li, Yuli Li, Yulin Li, Yuling Li, Yulong Li, Yumao Li, Yumei Li, Yumiao Li, Yumin Li, Yun Li, Yun-Da Li, Yun-Lin Li, Yun-Peng Li, Yun-tian Li, Yuna Li, Yunan Li, Yunchu Li, Yunfeng Li, Yunjiu Li, Yunlong Li, Yunlun Li, Yunman Li, Yunmin Li, Yunpeng Li, Yunqi Li, Yunrui Li, Yunshen Li, Yunsheng Li, Yunting Li, Yunxi Li, Yunxiao Li, Yunxu Li, Yunyun Li, Yunze Li, Yuping Li, Yuqi Li, Yuqian Li, Yuqing Li, Yuqiu Li, Yuquan Li, Yushan Li, Yutang Li, Yutian Li, Yuting Li, Yutong Li, Yuwei Li, Yuxi Li, Yuxiang Li, Yuxin Li, Yuxiu Li, Yuxuan Li, Yuyan Li, Yuying Li, Yuyun Li, Yuzhe Li, Yvonne Li, Z Li, Z-H Li, Zaibo Li, Ze Li, Ze-An Li, Zecai Li, Zechuan Li, Zehan Li, Zehua Li, Zejian Li, Zemin Li, Zengyang Li, Zequn Li, Zesong Li, Zexu Li, Zeyu Li, Zeyuan Li, Zezhi Li, Zhan Li, Zhandong Li, Zhang Li, Zhanjun Li, Zhankui Li, Zhanquan Li, Zhantao Li, Zhao Li, Zhao-Cong Li, Zhao-Yang Li, Zhaobing Li, Zhaohan Li, Zhaojin Li, Zhaoliang Li, Zhaolun Li, Zhaoping Li, Zhaosha Li, Zhaoshui Li, Zhaoyong Li, Zhe Li, Zhehui Li, Zhen Li, Zhen-Hua Li, Zhen-Jia Li, Zhen-Li Li, Zhen-Xi Li, Zhen-Yu Li, Zhen-Yuan Li, Zhenbei Li, Zhencheng Li, Zhencong Li, Zhenfei Li, Zhenfen Li, Zheng Li, Zheng-Dao Li, Zhengda Li, Zhenghao Li, Zhenghui Li, Zhengjie Li, Zhengliang Li, Zhenglong Li, Zhengnan Li, Zhengpeng Li, Zhengrui Li, Zhenguang Li, Zhengwei Li, Zhengyang Li, Zhengyao Li, Zhengying Li, Zhengyu Li, Zhenhao Li, Zhenhua Li, Zhenhui Li, Zhenjia Li, Zhenjun Li, Zhenli Li, Zhenlu Li, Zhenming Li, Zhenshu Li, Zhenyan Li, Zhenyu Li, Zhenzhe Li, Zhenzhou Li, Zheyun Li, Zhi Li, Zhi-Bin Li, Zhi-Gang Li, Zhi-Jian Li, Zhi-Peng Li, Zhi-Wei Li, Zhi-Xing Li, Zhi-Yong Li, Zhi-Yuan Li, Zhi-qiang Li, Zhibin Li, Zhichao Li, Zhifan Li, Zhifei Li, Zhigang Li, Zhigao Li, Zhihao Li, Zhihong Li, Zhihua Li, Zhihui Li, Zhijia Li, Zhijie Li, Zhijun Li, Zhilei Li, Zhimei Li, Zhiming Li, Zhipeng Li, Zhiping Li, Zhiqiang Li, Zhiqiong Li, Zhiquan Li, Zhirong Li, Zhisheng Li, Zhiwei Li, Zhixiong Li, Zhixuan Li, Zhiyang Li, Zhiyi Li, Zhiyong Li, Zhiyu Li, Zhiyuan Li, Zhizhong Li, Zhizong Li, Zhong Li, Zhong-Xin Li, Zhongcai Li, Zhongding Li, Zhonggen Li, Zhonghua Li, Zhongjie Li, Zhonglian Li, Zhonglin Li, Zhongwen Li, Zhongxia Li, Zhongxian Li, Zhongxuan Li, Zhongyu Li, Zhongzhe Li, Zhou Li, Zhouhua Li, Zhouxiang Li, Zhu Li, Zhuang Li, Zhuangzhuang Li, Zhuanjian Li, Zhuo Li, Zhuo-Rong Li, Zhuoran Li, Zhuorong Li, Zi-Zhan Li, Zichao Li, Zihai Li, Zihan Li, Zihao Li, Zihua Li, Zihui Li, Zijian Li, Zijing Li, Zili Li, Ziliang Li, Zilin Li, Zilu Li, Zimeng Li, Ziming Li, Zipeng Li, Ziqi Li, Ziqiang Li, Ziqing Li, Ziru Li, Zirui Li, Ziwen Li, Zixiao Li, Ziyang Li, Ziyu Li, Ziyue Li, Ziyun Li, Zizhuo Li, Zong-Xue Li, Zongchao Li, Zongdi Li, Zongfang Li, Zonghong Li, Zonghua Li, Zongjun Li, Zonglin Li, Zongyi Li, Zongyu Li, Zongyun Li, Zongzhe Li, Zu-Ling Li, Zu-guo Li, Zulong Li, Zunjiang Li, Zuo-Lin Li
articles
Rong Lin, Tong Guo, Bingjie Wei +2 more · 2026 · Journal of affective disorders · Elsevier · added 2026-04-24
Older adults in nursing homes generally face psychological adaptation problems such as depression and anxiety. This study aimed to identify social relationship profiles among nursing home residents an Show more
Older adults in nursing homes generally face psychological adaptation problems such as depression and anxiety. This study aimed to identify social relationship profiles among nursing home residents and explore their associations with depression and anxiety. A cross-sectional study was conducted between June and October 2023 among 1108 older residents from 42 nursing homes in Fujian Province, China. Social relationships were assessed using the Social Support Rating Scale (SSRS) and the Lubben Social Network Scale-6 (LSNS-6). Depressive-anxiety symptoms were measured using the Geriatric Depression Scale-Short Form and the Self-Rating Anxiety Scale (SAS), respectively. Latent Profile Analysis (LPA) was performed to identify distinct social relationship profiles, and ANOVA/ANCOVA were used to examine differences in depression and anxiety across profiles. The LPA analysis identified six distinct social relationship profiles. The "Low social support/low social network group" (24.7%) was the most prevalent, showing significantly higher levels of depression and anxiety compared to the others. The "Moderate social support/moderate friend network group" (20.9%) demonstrated an intermediate and balanced social relationship characteristic. When compared to the "Moderate-low social support/high friend network group" (8.1%) and the "Moderate-high social support/low friend network group" (18.1%), despite these two groups scoring higher or above-average in specific dimensions of social support or friend network, they still showed higher levels of depression than the "High social support/high social network group" (15.1%) and the "High social support/super high social network group" (13.1%). Social relationship profiles among nursing home residents are heterogeneous and significantly associated with depressive-anxiety symptoms. Show less
no PDF DOI: 10.1016/j.jad.2026.121788
LPA
Li-Bing Liang, Kun-Peng Li, Cai-Qin Wu · 2026 · European journal of preventive cardiology · Oxford University Press · added 2026-04-24
no PDF DOI: 10.1093/eurjpc/zwag181
LPA
Jinjing Zhao, Rufang Wang, Yongqiu Li +3 more · 2026 · BMC psychology · BioMed Central · added 2026-04-24
To explore the latent profiles of self-stigma and their relationship with meaning in life among individuals with substance use disorders(SUDs). A total of 1001 participants were recruited from six dru Show more
To explore the latent profiles of self-stigma and their relationship with meaning in life among individuals with substance use disorders(SUDs). A total of 1001 participants were recruited from six drug rehabilitation centers in Sichuan Province between July and August 2025 and completed the self-stigma Scale for Drug Addicts (SSSDA) and the Meaning in Life Questionnaire (MLQ). Latent profile analysis (LPA) was used to identify latent profiles of self-stigma. Multinomial logistic regression was employed to analyze influencing factors, and analysis of variance (ANOVA) was used to compare differences in meaning in life across the different profiles. The self-stigma of individuals with SUDs can be categorized into four latent profiles: the "stigma-resistant profile"(10.0%), "moderate stigma-concealment profile"(46.3%), "internalized stigma profile"(19.5%), and "low internalization-adaptation profile"(24.3%). Among these, the "moderate stigma-concealment profile", "internalized stigma profile", and "low internalization-adaptation profile" represent categories with higher levels of self-stigma. Risk factors associated with these profiles include male sex, low income, a history of being left-behind children, low social support, multiple rehabilitation attempts, as well as mental illness or HIV infection. Statistically significant differences were found among the four profiles in the total score of meaning in life and its sub-dimensions-presence of meaning and search for meaning (p < 0.001). The "stigma-resistant profile" presented the highest level of MIL, whereas the "internalized stigma profile" presented the lowest level. Significant heterogeneity exists in self-stigma among individuals with substance use disorders (SUDs), and the level of self-stigma is significantly negatively correlated with MIL. Show less
no PDF DOI: 10.1186/s40359-026-04187-0
LPA
Chan Cai, Bing Cheng, Chongqing Shi +4 more · 2026 · PloS one · PLOS · added 2026-04-24
The quality of informal care for people with dementia (PwD) has gained increasing importance, as most PwD prefer home-based care over institutional placement. However, evidence-based intervention prog Show more
The quality of informal care for people with dementia (PwD) has gained increasing importance, as most PwD prefer home-based care over institutional placement. However, evidence-based intervention programs tailored to distinct care quality profiles remain limited. Additionally, the absence of clear thresholds to identify PwD receiving low-quality informal care poses a challenge for research and clinical practice. Thus, this study aimed to identify the profiles of quality of care (QoC) among informal caregivers of PwD, explore influencing factors of different profile, and determine the optimal cut-off score of the Exemplary Care Scale (ECS). A cross-sectional survey was conducted. A total of 213 dyads of PwD and their informal caregivers were recruited from memory clinic, rehabilitation clinic, and neurological clinic of a tertiary hospitals and communities in Wuhan, Hubei, China, between July 15, 2023, and July 14, 2024. Latent profile analysis (LPA) was employed to identify QoC profiles. Multinomial logistic regression was performed to explore influencing factors of profile membership. Receiver Operating Characteristic (ROC) analysis was conducted to determine the ECS cut-off score. Three distinct QoC profiles were identified: high (24.41%), moderate (44.60%), and low (30.99%). Among informal caregivers, lower monthly income, insufficient social support, and higher perceived overload were associated with low QoC profile, whereas, better quality of pre-illness relationship with PwD and greater activities of daily living (ADL) of PwD were associated with high QoC. ROC analysis yielded an optimal ECS cut‑off score of 15, with high sensitivity (0.993) and specificity (0.955). This study identified three distinct QoC profiles among caregivers of PwD, underscoring the heterogeneity of informal care quality. The identified predictors and the validated ECS cut‑off score of 15 provide an empirical basis for developing tailored screening tools and targeted interventions for high‑risk caregiver subgroups. Show less
📄 PDF DOI: 10.1371/journal.pone.0346557
LPA
Xia Li, Fengling Yang, Xingyu Chen +2 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
This study employs latent profile analysis (LPA) to identify potential categories of nurse burnout and to analyze differences in characteristics and influencing factors across burnout categories. From Show more
This study employs latent profile analysis (LPA) to identify potential categories of nurse burnout and to analyze differences in characteristics and influencing factors across burnout categories. From June to August 2025, a mixed sampling approach combining convenience and snowball sampling was used to recruit nurses from hospitals of varying levels in Southwest China. Three tools were used for data collection: A self-designed routine information questionnaire, Maslach Burnout Inventory-General Survey (MBI-GS) and Practice Environment Scale of the Nursing Work Index (PES-NWI), LPA identifies potential categories of nurses' professional burnout and uses multivariate logistic regression analysis to explore the factors associated with these categories. This study comprised a total of 809 participants. LPA identified four distinct latent classes of nursing burnout: Class 1, low-burnout-high-efficacy (11.5%); Class 2, mild-burnout-unfulfilled (33.9%); Class 3, moderate-burnout-exhausted (44.6%); and Class 4, severe-burnout-dysfunctional (10.0%). Multivariate logistic regression analysis showed that age, years of work experience, hospital level, nurses' participation in hospital management, nursing quality standards, staffing and resource adequacy, and medical care cooperation are significant predictors of burnout among nurses ( Nurse burnout in southwest China is mainly moderate to severe and exhibits distinctive characteristics. It is recommended to implement personalized interventions tailored to the specific characteristics of nurses' professional burnout to alleviate the situation. Particular attention should be given to nurses with fewer than five years of experience by providing enhanced job support and psychological assistance to help them navigate critical periods of professional burnout. These measures aim to safeguard nurses' physical and mental health, improving the overall quality of nursing, and promoting the healthy development of global medical care. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1764970
LPA
Yinhu Tan, Hang Li, Shuangxin Zhang +5 more · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Frailty is associated with increased risks of falls, disability, hospitalization, and mortality. The 24-h movement behaviors (24HMB) framework conceptualizes sleep, sedentary behavior (SB), light-inte Show more
Frailty is associated with increased risks of falls, disability, hospitalization, and mortality. The 24-h movement behaviors (24HMB) framework conceptualizes sleep, sedentary behavior (SB), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) as mutually constrained components of daily time use and may inform frailty prevention and management. This scoping review maps evidence on associations between 24HMB and frailty and identifies methodological gaps to inform future research and nursing practice. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and follows Joanna Briggs Institute (JBI) guidance. We searched PubMed, Embase, CINAHL, and Web of Science. We included observational studies of adults aged ≥18 years. Exposures were objectively measured or validated self-reported sleep, SB, LPA, and MVPA, including step counts, breaks in SB, isotemporal substitution models (ISM), and compositional data analysis (CoDA). Outcomes were frailty or prefrailty assessed using validated instruments. Quality was appraised with JBI tools. Thirty-three studies showed good methodological quality. Longer SB, particularly prolonged, uninterrupted bouts, was associated with higher frailty. Greater MVPA was consistently associated with lower frailty. Light-intensity physical activity was generally beneficial but often attenuated when MVPA or total activity volume was modeled. Sleep fragmentation and poor sleep quality were associated with frailty. Isotemporal substitution models and compositional data analysis indicated that reallocating sedentary time to MVPA would yield the largest theoretical benefit, followed by reallocating to LPA. Higher daily step counts and more frequent or higher-intensity breaks in SB were associated with lower frailty. Evidence supports a 24-h integrated movement-behavior approach centered on MVPA, combined with reducing prolonged SB and improving sleep quality, for the prevention and nursing management of frailty. The study design and analytical protocol were prospectively registered on the Open Science Framework (OSF). The unique identifier is S39Y4, and the publicly accessible URL is https://doi.org/10.17605/OSF.IO/S39Y4. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1780746
LPA
Yue Peng, Yan Pu, Yuyang Wang +3 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
To ascertain the level of psychological resilience, examine the latent profiles of individuals within infertile couples who experience recurrent implantation failure (RIF), identify the relevant influ Show more
To ascertain the level of psychological resilience, examine the latent profiles of individuals within infertile couples who experience recurrent implantation failure (RIF), identify the relevant influencing factors, and lay a foundation for developing customized intervention strategies. Convenience sampling was adopted in this study. Participants were selected from individuals in infertile couples with RIF who attended the Second West China Hospital of Sichuan University between November 2024 and July 2025. Data were collected via a general information questionnaire and validated scales assessing psychological resilience, social support, sleep quality, family adaptability and cohesion, anxiety, and depression. Latent profile analysis (LPA) was performed to explore the psychological resilience profiles of individuals with RIF, while univariate analysis and multivariate Logistic regression analyses were employed to identify the influencing factors associated with different profile categories. A total of 303 valid questionnaires were collected, including 194 from females and 109 from males. The overall psychological resilience score was (26.66 ± 6.319). Latent profile analysis categorized psychological resilience into three subgroups: the low tenacity-low strength subgroup (31.4%), the moderate tenacity-moderate strength subgroup (53.1%), and the high tenacity-high strength subgroup (15.5%); Multivariate Logistic regression analysis indicated that gender, family adaptability and depression severity (all Marked interindividual heterogeneity exists in the psychological resilience of individuals with RIF. Gender, family adaptability and depression severity serve as the core influencing factors. In clinical practice, stratified and targeted interventions should be delivered according to distinct psychological resilience subgroups. It yields clinical implications for an association between improved psychological resilience among individuals from couples with RIF and enhanced treatment adherence. Show less
📄 PDF DOI: 10.3389/fpsyt.2026.1798373
LPA
Kuiliang Li, Lei Ren, Rui Lang +7 more · 2026 · Stress and health : journal of the International Society for the Investigation of Stress · Wiley · added 2026-04-24
Compared with non-left-behind children (NLBC), left-behind children (LBC) face a higher risk of academic stress, depression, and anxiety symptoms due to separation from their parents; however, the het Show more
Compared with non-left-behind children (NLBC), left-behind children (LBC) face a higher risk of academic stress, depression, and anxiety symptoms due to separation from their parents; however, the heterogeneity of academic stress profiles and their relationships with the symptom network remain insufficiently explored. To address this gap, a cross-sectional survey of 10,524 Chinese children compared LBC (n = 2487) and NLBC. Latent profile analysis (LPA) was first conducted to identify academic stress subgroups among LBC. Subsequently, depression-anxiety symptom networks were estimated using Ising and Gaussian graphical models (GGM), with edge weights derived from regularised logistic regression (Ising) and partial correlation (GGM). Simulated interventions were further evaluated via the NodeIdentifyR algorithm (NIRA). Overall, compared to NLBC, LBC exhibited higher levels of academic stress, depression, and anxiety (ps < 0.001, Cliff's δ = 0.076; Cohen's d = 0.067). LPA revealed three academic stress subgroups: moderate (31.44%), high (9.17%), and low (59.39%). The severity of depression and anxiety symptoms increased with the level of academic stress. The high stress subgroup displayed a sparse network with stronger edges (e.g., A1 'Sudden Fear'-A4 'Physical Symptoms', edge weight = 2.10) compared to moderate- and low-academic stress subgroups. Core nodes with the strongest expected influence were A8 ('Decision Hesitation', moderate subgroup), A2 ('Worry', high subgroup), and D1/D6 ('Sadness' and 'Failure', low subgroup). Simulated interventions indicated that alleviating A8 'Decision Hesitation' or A2 'Worry' most effectively reduced symptom risk (16.66%-30.76%), whereas D8 'Motor' and A7 'Early Departure' were associated with maximal symptom aggravation. Taken together, by integrating LPA-derived academic stress profiles with symptom network analysis, this study reveals distinct symptom associations across subgroups. In the high stress subgroup, symptom A2 ('Worry') is a core intervention target; in the low stress subgroup, A7 ('Early Departure') holds preventive potential. These findings underscore subgroup-specific interventions tailored to individual stress profiles. Show less
no PDF DOI: 10.1002/smi.70172
LPA
Wen Guo, Fei Lin, Chengxiao Yu +5 more · 2026 · Frontiers in nutrition · Frontiers · added 2026-04-24
Given that abnormal lipid metabolism is a hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), this study seeks to investigate the relationship between serum lipoprotein(a) [L Show more
Given that abnormal lipid metabolism is a hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), this study seeks to investigate the relationship between serum lipoprotein(a) [Lp(a)] levels and the progression or regression of MASLD. A total of 12,962 participants undergoing transient elastography at the Health Promotion Center of the First Affiliated Hospital of Nanjing Medical University were included in the first cross-sectional study (Study 1). The longitudinal study (Study 2) included 17,661 individuals from the same center, each with at least two health check-ups involving abdominal ultrasonography. Another cross-sectional study (Study 3) included 5,927 individuals from the UK Biobank cohort who had undergone both magnetic resonance imaging proton density fat fraction (MRI-PDFF) and Lp(a) testing. Cross-sectional analysis (Study 1) revealed that elevated Lp(a) levels were inversely correlated with the severity of both hepatic steatosis and fibrosis. Longitudinal data (Study 2) further demonstrated that baseline serum Lp(a) levels were decreased in participants with the incident of MASLD, while increased in participants with the regression of MASLD during the follow-up period. A lower baseline Lp(a) level was an independent factor for new-onset MASLD and non-regression of MASLD: the fully adjusted hazard ratios (HR) were 0.895 (95%CI 0.834-0.962, Serum Lp(a) levels are inversely associated with both the progression and regression of MASLD, indicating its potential role in reflecting disease dynamics. Show less
📄 PDF DOI: 10.3389/fnut.2026.1722393
LPA
Yingying Zhao, Jiayi Luo, Kai Xu +2 more · 2026 · Reviews in cardiovascular medicine · added 2026-04-24
This study aimed to explore the association between serum lipoprotein(a) [Lp(a)] levels and recurrent acute coronary syndrome (ACS) and revascularization of target lesions in patients with ACS who sho Show more
This study aimed to explore the association between serum lipoprotein(a) [Lp(a)] levels and recurrent acute coronary syndrome (ACS) and revascularization of target lesions in patients with ACS who showed no functional ischemia on fractional flow reserve (FFR) testing during coronary angiography (CAG). The retrospective observational study was conducted at the General Hospital of Northern Theater Command and included 513 patients with new ACS recruited from 23 February 2016 to 6 November 2023 and followed up. These patients underwent CAG examination and were found to have at least one coronary artery with moderate or greater stenosis, and also underwent FFR measurement with FFR value >0.80. Patients experienced recurrent ACS and underwent unplanned revascularization were defined as the revascularization group, while patients did not experience recurrent ACS and undergo unplanned revascularization were assigned to the no revascularization group. The study employed propensity score matching (PSM) and receiver operating characteristic (ROC) curve analysis to evaluate the correlation between serum Lp(a) and recurrent ACS and unplanned revascularization in target lesion with FFR value >0.80. Serum Lp(a) levels were higher in female patients. There were no statistically significant differences in the basic clinical characteristics, medication use, laboratory test results or ejection fraction values between the two groups. During a average follow-up of 6.5 years, 119 patients (23.2%) experienced recurrent ACS and unplanned revascularization in the target lesion. The level of serum Lp(a) in the patients that underwent unplanned revascularization was significantly higher than in the group that did not undergo repeated revascularization (65.80 mmol/L vs. 60.57 mmol/L, Serum Lp(a) is an independent risk factor for recurrent ACS and unplanned revascularization in patients with ACS and FFR negative plaque. Show less
📄 PDF DOI: 10.31083/RCM47169
LPA
Tian Tian, Bin Hu, Xin-Tao Li +5 more · 2026 · European journal of pharmacology · Elsevier · added 2026-04-24
It remains unclear if Yes-associated protein (YAP) is involved in the protection of melatonin against myocardial ischemia/reperfusion (I/R) injury by regulating mitochondrial fission. In this experime Show more
It remains unclear if Yes-associated protein (YAP) is involved in the protection of melatonin against myocardial ischemia/reperfusion (I/R) injury by regulating mitochondrial fission. In this experiment, an in vivo myocardial I/R injury model was used. Animals were randomly assigned to receive the different interventions: Sham, I/R, 10 mg melatonin, 20 mg melatonin, lysophosphatidic acid (LPA, a YAP agonist), LPA + melatonin, verteporfin (a YAP antagonist) and verteporfin + melatonin. Myocardial infarct size and serum cardiac enzyme levels were measured. Histopathological features, mitochondrial morphology, malondialdehyde (MDA) and superoxide dismutase (SOD) levels, apoptosis, and dynamic-related protein 1 (DRP1) and YAP expressions of the I/R myocardium were also evaluated. We observed that melatonin postconditioning significantly reduced myocardial infarct size, ameliorated histological changes, and decreased oxidative stress and apoptosis in the I/R myocardium. These protective effects were associated with enhanced YAP nuclear translocation, increased p-DRP1 Ser637 expression and decreased p-DRP1 Ser616 expression. Activation of YAP with LPA demonstrated a protective effect against myocardial I/R injury, while inhibition of YAP with verteporfin exacerbated myocardial I/R injury and significantly attenuated the protective effect of melatonin postconditioning against myocardial I/R injury. These findings suggest that melatonin postconditioning confers cardioprotection by activating YAP to preserve mitochondrial ultrastructure and attenuate excessive DRP1-mediated fission. These structural changes may contribute to the observed reduction in myocardial injury. While these findings identify YAP activation as a potential therapeutic target, the limited dose range tested precludes determination of an optimal cardioprotective dose. Further studies defining the full dose-response relationship are still necessary to inform potential clinical translation. Show less
no PDF DOI: 10.1016/j.ejphar.2026.178827
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
Daniel Ezzat, Diana M Lopez, Brian L Claggett +13 more · 2026 · European heart journal · Oxford University Press · added 2026-04-24
Elevated lipoprotein(a) [Lp(a)] levels are an established risk factor for atherosclerotic cardiovascular disease, but the association between Lp(a) and venous thromboembolism (VTE) remains unclear. Se Show more
Elevated lipoprotein(a) [Lp(a)] levels are an established risk factor for atherosclerotic cardiovascular disease, but the association between Lp(a) and venous thromboembolism (VTE) remains unclear. Sex and hormonal status may modify the relationship between Lp(a) and VTE. The present study included participants from the UK Biobank with available baseline Lp(a) data. Individuals with a history of VTE or cancer, as well as those using anticoagulants, were excluded. Multivariable-adjusted Cox models were used to assess the association between Lp(a) levels ≥ 125 nmol/L and incident VTE in premenopausal women, postmenopausal women, and men. Subgroup analyses stratified premenopausal women by oral contraceptive (OCP) use and postmenopausal women by menopausal hormone therapy (MHT) use. Among 55 302 premenopausal women, 129 045 postmenopausal women, and 189 013 men, the proportions with Lp(a) ≥ 125 nmol/L were 14.0%, 19.0%, and 15.0%, respectively. Over a median (interquartile range) follow-up of 13.6 (12.9-14.4) years, 8186 VTE events occurred (cumulative incidence 2.2%). Lp(a) ≥ 125 nmol/L was associated with incident VTE in premenopausal women [adjusted hazard ratio (aHR) 1.32; 95% confidence interval (CI) 1.04-1.66; P = 0.02] but not in postmenopausal women (aHR 1.03; 95% CI 0.94-1.13; P = 0.47; Pinteraction = 0.03) or men (aHR 1.00; 95% CI 0.92-1.08; P = 0.94). OCP use did not modify the Lp(a)-VTE association among premenopausal women (Pinteraction = 0.61). However, among postmenopausal MHT users, Lp(a) ≥ 125 nmol/L was associated with higher VTE risk (aHR 1.48; 95% CI 1.03-2.12; P = 0.03; Pinteraction = 0.04). Elevated Lp(a) was associated with VTE in premenopausal women and in postmenopausal MHT users, suggesting that hormonal context may influence Lp(a)- associated thrombotic risk. Show less
no PDF DOI: 10.1093/eurheartj/ehag252
LPA
Meizhu Ding, Yinggao Li, Shasha Yao +1 more · 2026 · Annals of vascular surgery · Elsevier · added 2026-04-24
The pathogenesis of aortic aneurysm (AA) remains unclear, and there are no effective therapeutic drugs or targets. Circulating plasma proteins are considered biomarkers of AA and potential therapeutic Show more
The pathogenesis of aortic aneurysm (AA) remains unclear, and there are no effective therapeutic drugs or targets. Circulating plasma proteins are considered biomarkers of AA and potential therapeutic targets for AA. This study aimed to systematically evaluate the causal effects of plasma proteins on AA using a multi-cohort Mendelian randomization (MR) approach. Protein quantitative trait loci (pQTLs) was obtained from 9 published proteome genome-wide association studies (GWAS) and AA GWAS data from the FinnGen cohort. Independent pQTLs were selected as instrumental variables (IVs). Two-sample MR analysis was performed using inverse-variance weighted, MR-Egger regression, weighted median, weighted mode, and simple mode methods. Heterogeneity and pleiotropy were assessed using Cochran's Q test, I A total of 8,285 pQTLs for 4,421 proteins were retained as IVs. Using cis-pQTLs for IVs,MR analysis identified 154 proteins associated with thoracic aortic aneurysm (TAA; 76 protective and 78 risk factors) and 211 proteins with abdominal aortic aneurysm (AAA; 112 protective and 99 risk factors) Using cis-pQTLs combined with trans-pQTLs as IVs, MR analysis identified 236 proteins associated with TAA and 309 proteins with AAA. A subset of these associations survived FDR correction (FDR < 0.05), representing the most robust findings. Comparison of the TAA and AAA proteomic profiles revealed both shared proteins (e.g., AHSG, MMP7, RARRES2, THBS2, CCL25) and condition-specific proteins (e.g., OVCA2, STAT3, and HPSE for TAA; PLAU, LPA, SERPING1, and SMPDL3A for AAA), reflecting the distinct embryonic origins and pathological drivers of these two conditions. Steiger filtering confirmed the expected direction of effect from circulating proteins to AA. Colocalization analysis found evidence of shared causal variants between multiple proteins and AA. Pathway enrichment analysis revealed involvement in stress response, immune regulation, cytokine-cytokine receptor interaction, and metabolic processes. Nearly two-thirds of the associated proteins were classified as druggable or potentially druggable targets. This study identified a large number of potentially novel pathogenic proteins and therapeutic targets for AA, providing important references for elucidating the molecular pathogenesis of AA and advancing drug development. These findings warrant further validation through experimental studies and prospective clinical investigations. Show less
no PDF DOI: 10.1016/j.avsg.2026.03.008
LPA
Shuqin Hong, Xiuni Gan, Wen Zhou +8 more · 2026 · Patient preference and adherence · added 2026-04-24
To describe the network structure and heterogeneity of symptom burden in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI), and to examine factors associated w Show more
To describe the network structure and heterogeneity of symptom burden in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI), and to examine factors associated with different symptom burden profiles to inform risk-stratified management after PCI. A convenience sample of 261 patients with ACS who underwent PCI at a tertiary hospital in Chongqing between November 2024 and August 2025 was recruited. Data were collected using a demographic questionnaire, the Cardiac Symptom Survey, and the Seattle Angina Questionnaire. Network analysis was conducted to identify inter-symptom associations and the structural characteristics of the symptom network. Latent profile analysis (LPA) was performed to classify symptom burden patterns, and multinomial logistic regression analysis was used to explore factors associated with profile membership. Network analysis indicated that depression was the most central symptom (strength Symptom burden in patients with ACS after PCI demonstrates substantial individual heterogeneity. Depression occupies a central position within the symptom network, and BMI is associated with moderate and high symptom burden profiles. These findings suggest that integrating symptom network characteristics and BMI status into post-PCI assessment may facilitate risk-stratified management and targeted psychological and weight-related interventions to improve recovery outcomes. Show less
📄 PDF DOI: 10.2147/PPA.S580130
LPA
Yaojia Li, Yang Li, Xin Ye +1 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
This study employed a person-centered approach to identify latent profiles of academic burnout among Chinese university students and to examine the associations between academic burnout profiles and s Show more
This study employed a person-centered approach to identify latent profiles of academic burnout among Chinese university students and to examine the associations between academic burnout profiles and smartphone addiction, sleep quality, and mindfulness. A sample of 2,948 Chinese university students was recruited to complete measures of academic burnout, smartphone addiction, sleep quality, and mindfulness. Latent profile analysis (LPA) was used to identify distinct burnout profiles, and multinomial logistic regression was used to analyze factors associated with profile membership. Three distinct profiles of academic burnout were identified: a Low Burnout profile (18.15%), a Medium Burnout profile (50.88%), and a High Burnout profile (30.97%). The profiles differed significantly on all correlates, with the high burnout group exhibiting the most severe smartphone addiction, the poorest sleep quality, and the lowest mindfulness. Regression analysis revealed that higher smartphone addiction and poorer sleep quality were significantly associated with membership in the Medium and High Burnout profiles relative to the Low Burnout profile, whereas higher mindfulness was significantly associated with lower likelihood of belonging to higher burnout profiles. Academic burnout among Chinese university students is a heterogeneous experience, with a majority falling into an at-risk or intermediate state. Smartphone addiction, poor sleep, and low mindfulness are associated with higher burnout risk. These findings highlight the need for universities to develop targeted, profile-based interventions to provide precise and effective mental health support. However, due to the cross-sectional design, causal relationships cannot be inferred. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1701455
LPA
Li Zhang, Fengyi Li, Yaru Wu +3 more · 2026 · Cancer management and research · added 2026-04-24
This study aims to identify distinct mindfulness profiles among young and middle-aged lymphoma patients and to examine the mediating role of psychological resilience in the relationship between these Show more
This study aims to identify distinct mindfulness profiles among young and middle-aged lymphoma patients and to examine the mediating role of psychological resilience in the relationship between these mindfulness profiles and social function deficits. From November 2024 to June 2025, a total of 324 young and middle-aged lymphoma patients were recruited using convenience sampling from a tertiary cancer hospital in Urumqi, Xinjiang, China. Participants completed the Mindful Attention Awareness Scale, the 10-item Connor-Davidson Resilience Scale, and the Social Dysfunction Screening Scale. We used latent profile analysis (LPA) to identify distinct mindfulness profiles and tested the mediating role of psychological resilience with the Bootstrap method. Latent profile analysis identified three distinct mindfulness profiles among the patients: a low mindfulness type (29.3%), a moderate mindfulness type (40.1%), and a high mindfulness type (30.6%). Furthermore, psychological resilience partially mediated the relationship between these mindfulness profiles and social function deficits. Young and middle-aged lymphoma patients exhibit heterogeneous mindfulness profiles. Higher mindfulness can enhance psychological resilience, which in turn alleviates social function deficits. Therefore, healthcare providers should develop personalized interventions targeting psychological resilience based on patients' specific mindfulness profiles to improve their social function. Show less
📄 PDF DOI: 10.2147/CMAR.S570129
LPA
Dinuo Xin, Dina Xin, Ying Wang +3 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
This study aimed to investigate the current status of career calling among novice nurses, to identify potential subtypes and their population characteristics, and to further explore the factors associ Show more
This study aimed to investigate the current status of career calling among novice nurses, to identify potential subtypes and their population characteristics, and to further explore the factors associated with the different subtypes. A cross-sectional descriptive study was used. From January to February 2024, 845 novice nurses from 11 hospitals in Shanxi Province were selected for an online questionnaire survey using convenience sampling. The demographic questionnaire, transition shock of newly graduated nurses scale, medical staff resilience scale, and career calling scale were used as study instruments. Latent profile analysis (LPA) was used to explore the subtypes of novice nurses' career calling, and multifactorial logistic regression was used to analyze the related factors of novice nurses' career calling. Three subtypes of career calling of novice nurses in this study were identified, namely, lacking-calling group (10.3%), stable-calling group (50.0%), and sufficient-calling group (39.7%). Education, weekly working hours, weekly frequency of night shifts, reasons for choosing nursing, level of transition shock, and level of resilience were significantly associated with the three latent profiles of career calling of novice nurses in this study. Novice nurses' career calling presents 3 latent profiles and is heterogeneous in this study. Nursing administrators could pay attention to the differences in the level of career calling of novice nurses and adopt targeted management strategies based on the type of characteristics of the population in order to improve the level of career calling of novice nurses, help them develop their careers, and stabilize the nursing workforce. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1651190
LPA
Yunyun Liu, Xiangrui Li, Ting Zhao +9 more · 2026 · Frontiers in psychology · Frontiers · added 2026-04-24
Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile an Show more
Fear of progression (FoP) is a prevalent psychological issue among stroke patients. Previous studies failing to distinguish characteristics of patient groups with varying FoP levels. Latent profile analysis (LPA) classifies individuals into distinct subgroups via continuous FoP indicators, boosting classification accuracy by accounting for variable uncertainty. Given FoP's heterogeneity, investigating FoP profiles and their influencing factors in stroke patients is clinically significant for personalized psychological care and improved patient quality of life. A total of 366 stroke patients were selected as study subjects through convenience sampling, and a cross-sectional survey was conducted. FoP was assessed using the Fear of Progression Questionnaire-Short Form (FoP-Q-SF, 2 dimensions, 12 items). Independent variables included demographic characteristics, clinical indicators, the Recurrence Risk Perception Scale for Stroke patients (RRPSS), and the Medical Coping Modes Questionnaire (MCMQ). LPA was performed on the FoP-Q-SF items to identify subgroups. The R3STEP method was used to analyze influencing factors of subgroup membership, and the BCH method was applied to compare differences in distal outcomes across subgroups. Statistical significance was set at The study sample had a mean age of 63.93 ± 10.58 years, with 70.5% males and 65.0% first-ever stroke patients. Two latent profiles were identified: Low-FoP Adaptive Type (C1, 48.6%) and High-FoP Sustained Type (C2, 51.4%). The R3STEP showed that age 18-59 years (OR = 0.476, 95%CI = 0.245-0.924, This study revealed significant heterogeneity in FoP among stroke patients. Age, hypertension comorbidity, excessive recurrence risk perception, MCMQ-confrontation, and MCMQ-avoidance were associated with high FoP. Healthcare providers should prioritize identifying high-risk individuals and develop tailored interventions to reduce FoP and improve rehabilitation outcomes. Show less
📄 PDF DOI: 10.3389/fpsyg.2026.1741344
LPA
Luling Zhou, Lingzhi Yang, Xiaoyi Zhu +3 more · 2026 · Psycho-oncology · Wiley · added 2026-04-24
Family members of patients with digestive tract cancer represent a high-risk population for cancer development due to shared genetic and lifestyle factors, yet their own disease self-monitoring behavi Show more
Family members of patients with digestive tract cancer represent a high-risk population for cancer development due to shared genetic and lifestyle factors, yet their own disease self-monitoring behaviors remain largely uncharacterized. Understanding the typologies and determinants of these behaviors is essential for precision prevention. A cross-sectional study was conducted among 414 family members of hospitalized patients with esophageal, gastric, or colorectal cancer in Sichuan Province, China (March-October 2023). Self-reported data were collected using validated questionnaires assessing socio-demographics, cancer risk perception, and digestive tract cancer self-monitoring behaviors. Latent profile analysis (LPA) was applied to identify subgroups of monitoring behaviors, and multinomial logistic regression was used to determine influencing factors. LPA revealed three distinct behavioral profiles: poor behavior group (47.10%), average behavior group (38.16%), and good behavior group (14.74%). The mean total self-monitoring score was 2.76 ± 0.69. Multivariate analysis showed that low educational level, family per capita monthly income ≤ 2000 CNY, and not living with patient were significant risk factors for poor monitoring behaviors. Conversely, having existing chronic disease and higher cancer risk perception were strongly associated with better monitoring performance. Nearly half of family members of digestive tract cancer patients exhibit insufficient self-monitoring of early symptoms. Education level, family per capita monthly income, cohabitation, comorbidity, and cancer risk perception are key determinants of behavioral heterogeneity. Tailored, risk-profile-based interventions that enhance risk awareness and promote regular screening are urgently needed to strengthen family-centered cancer prevention. Show less
no PDF DOI: 10.1002/pon.70432
LPA
Hui Zhou, Weilong Xiao, Xinwei Li · 2026 · Journal of affective disorders · Elsevier · added 2026-04-24
Problematic mobile phone use (PMPU) has become a prominent public health concern among Chinese adolescents and emerging adults, yet prior research has largely relied on variable-centered approaches th Show more
Problematic mobile phone use (PMPU) has become a prominent public health concern among Chinese adolescents and emerging adults, yet prior research has largely relied on variable-centered approaches that overlook within-group heterogeneity and provide limited insight into multilevel mechanisms. To address these gaps, this study adopted an integrated analytic framework combining latent profile analysis (LPA), structural equation modeling (SEM), and psychological network analysis. A total of 2345 Chinese university students completed measures of alexithymia (TAS-20), social interaction anxiety (IAS), and PMPU (MPAI). LPA identified three distinct PMPU profiles: Low-risk (62.7%), moderate-risk (24.8%), and high-risk (12.5%). SEM results indicated that alexithymia was positively associated with PMPU in the overall sample, with social interaction anxiety partially mediating this association. Profile-specific analyses further showed that the indirect pathway was significant in the low-risk and moderate-risk profiles but not in the high-risk profile, in which only a direct effect emerged. Network analyses in the low- and moderate-risk groups revealed profile-specific central and bridge nodes, primarily IAS items, highlighting potential symptom targets linking alexithymia and PMPU. Overall, findings underscore meaningful heterogeneity in PMPU and support profile-tailored prevention and intervention strategies emphasizing emotion-processing skills and social anxiety reduction. Show less
no PDF DOI: 10.1016/j.jad.2026.121612
LPA
Ying Li, Jieling Huang, Liuliu Kong +1 more · 2026 · Frontiers in psychiatry · Frontiers · added 2026-04-24
Improving Internet addiction among nursing students is of great significance to the future development of the nursing industry. Previous studies have proved that childhood trauma is closely related to Show more
Improving Internet addiction among nursing students is of great significance to the future development of the nursing industry. Previous studies have proved that childhood trauma is closely related to Internet addiction. However, the direct relationship between alexithymia and childhood trauma and Internet addiction has not been fully explored. The aim of this study is to identify different subgroups of nursing students based on their childhood trauma and to examine the mediating role of alexithymia between childhood trauma and Internet addiction. From April to May 2025, 3,697 nursing students were recruited as samples from Shandong, Hubei, Hunan, and Henan provinces in China by convenient sampling. This survey collected social demographic data. Including The Childhood Trauma Questionnaire - Short Form (CTQ-SF), the Toronto Alexithymia Scale (TAS-26), and the Internet addiction Scale. Potential profile analysis was used to determine the potential categories of childhood trauma characteristics of nursing students, and Pearson correlation analysis, Bayesian factor robustness analysis and mediation analysis were used to determine the potential relationships among variables. LPA identified three distinct groups based on their dominant usage: low (77.4%), medium (19.5%), and high (3.1%). In the relationship between childhood trauma and Internet addiction based on potential profile analysis, alexithymia has a significant mediating effect (SE = 0.442,95%CI = 0.095, 1.824; SE = 0.219, 95%CI = 0.093, 0.962). There is heterogeneity in childhood trauma among nursing students. Alexithymia plays an important mediating role in the relationship between childhood trauma and Internet addiction. It is suggested that nursing educators pay attention to the differences in childhood trauma among nursing students, provide corresponding psychological counseling for different students, improve them, thereby alleviating Internet addiction among nursing students and promoting their mental health. Show less
📄 PDF DOI: 10.3389/fpsyt.2026.1734868
LPA
Yunfeng Song, Liquan Cao, Sijie Tan +2 more · 2026 · Wei sheng yan jiu = Journal of hygiene research · added 2026-04-24
This study examined the associations between 24-hour movement behaviors and health-related fitness in university students, and estimated the substitution effects using a compositional isotemporal subs Show more
This study examined the associations between 24-hour movement behaviors and health-related fitness in university students, and estimated the substitution effects using a compositional isotemporal substitution model. This study was conducted between May and June 2023, using a combination of convenience and random sampling to recruit 325 undergraduate students from Tianjin University of Science & Technology as participants, including 167 males and 158 females. Daily 24-hour activity behaviors were measured using a triaxial accelerometer(ActiGraph GT3X+), including moderate-intensity physical activity(MPA), vigorous-intensity physical activity(VPA), light-intensity physical activity(LPA), sedentary behaviors(SB), and sleep(SLP) duration. Body composition was assessed via body mass index(BMI), waist circumference, and body fat percentage. Muscular strength was measured by handgrip strength, cardiorespiratory fitness was measured by vital capacity and maximum oxygen uptake(VO₍₂ max)), and flexibility was assessed by the sit-and-reach test. Compositional data analysis was used to investigate the associations between activity behaviors and health-related physical fitness. A 15-minute isotemporal substitution model was applied to predict the effects of replacing one activity with another on outcome variables. The mean age of male participants was(19.74±1.16) years, and that of female participants was(19.51±1.29) years. Based on 24-hour compositional activity behavior analysis, college students spent an average of 16.42 minutes(1.14% of the day) in MPA, 26.57 minutes(1.85%) in VPA, 150.92 minutes(10.48%) in LPA, 645.78 minutes(46.05%) in SB, and 561.31 minutes(40.21%) in SLP. After adjusting for covariates including sex and age, isotemporal substitution models revealed that replacing an equivalent amount of sedentary time with MPA was associated with a reduction in BMI by 0.07-0.19 units, body fat percentage by 0.53-0.59 units, waist circumference by 0.16-0.27 cm, an increase in vital capacity by 119.18-152.67 mL, VO₍₂ max) by 1.76-1.88 mL/(kg·min), handgrip strength by 0.86-1.46 kg, and sit-and-reach performance by 0.19-0.38 cm. Similarly, increasing VPA led to decreases in BMI by 0.14-0.16 units, body fat percentage by 0.49-0.54 units, waist circumference by 0.12-0.23 cm, increases in vital capacity by 127.45-160.84 mL, VO₍₂ max) by 1.91-2.03 mL/(kg·min), handgrip strength by 0.98-1.56 kg, and sit-and-reach by 0.14-0.32 cm. Increasing LPA result ed in BMI increases of 0.11-0.12 units, handgrip strength increases of 0.65 kg, and sit-and-reach increases of 0.21 cm. Increasing SLP was associated with BMI reduction of 0.04 units and waist circumference reduction of 0.09 cm. MPA had the most significant effect on improving BMI, body fat percentage, and waist circumference, while VPA was more effective in enhancing cardiorespiratory fitness, muscular strength, and flexibility. SLP had a modest positive effect on BMI and waist circumference but was less impactful than MPA and VPA. SB and LPA were generally unfavorable for health-related physical fitness. Show less
no PDF DOI: 10.19813/j.cnki.weishengyanjiu.2026.01.011
LPA
Chaowei Fang, Bolin Fu, De Cheng +2 more · 2026 · IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · IEEE · added 2026-04-24
Continual image super-resolution (CISR) aims to efficiently adapt a pre-trained model to a variety of tasks while retaining knowledge from previously learned tasks, minimizing the need for intensive i Show more
Continual image super-resolution (CISR) aims to efficiently adapt a pre-trained model to a variety of tasks while retaining knowledge from previously learned tasks, minimizing the need for intensive independent training. The primary challenges include catastrophic forgetting due to varying data distributions and degradation types, along with the necessity for high adaptability. While prompt-based continual learning has proven effective in image classification, its direct application to super-resolution (SR) often fails to meet the demands for detailed pixel-level restoration and domain discrimination in low-level characteristics. To address these challenges, we propose Learning Prompt Adapters (LPA), which dynamically generates pixel-wise prompts through a combination of multi-granularity prompt bases and identities. By adaptively integrating these prompts into the Transformer architecture, we effectively improve the model's performance on fine-grained details in super-resolution tasks, as well as enhancing the model's adaptability to new tasks and preserving knowledge from previous ones. Through organizing the low-rank prompt bases with specific identities, we set up an effective solution to managing cross-task differences and enhancing prompt richness. Extensive experiments on benchmarks comprising the NYU, RealSR, DIV2K, REDS, and MANGA109 datasets with diverse degradation types demonstrate that LPA significantly outperforms existing continual learning methods. Codes of this paper are available at: https://github.com/dummerchen/LPA. Show less
no PDF DOI: 10.1109/TIP.2026.3671688
LPA
Xintong Ma, Wei Li, Yuanyuan Liu +8 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24
Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-l Show more
Adolescence is a critical period for rapid emotional and cognitive development. Depression and cognitive impairment frequently co-occur in this population, yet their comorbidity patterns and symptom-level interactions remain insufficiently explored. A total of 2,244 students (mean age = 16.8 ± 0.84 years; 1,218 males, 1,026 females) from a high school in Heilongjiang Province, China, were recruited. Depressive symptoms and cognitive impairment were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Perceived Deficits Questionnaire–Depression (PDQ-D). Latent profile analysis (LPA) was applied to identify subgroups, followed by network analysis to examine central symptoms (expected influence, EI), bridge symptoms (bridge expected influence, BEI), and network differences (NCT). The optimal LPA model identified three comorbidity subgroups: low, moderate, and high. NCT revealed significant differences in network structure and global strength between the low–moderate (S = 1.514, Adolescent Depression and Cognitive Impairment can be classified into low, moderate, and high comorbidity subgroups. Somatic symptoms emerged as the central symptom, while prospective memory impairment and interpersonal problems were identified as key bridge symptoms, suggesting potential intervention targets for early screening and stratified treatment. Not applicable. The online version contains supplementary material available at 10.1186/s12888-026-07946-w. Show less
📄 PDF DOI: 10.1186/s12888-026-07946-w
LPA
Fei Gao, Kexin Ren, Bingbing Fan +2 more · 2026 · BMC geriatrics · BioMed Central · added 2026-04-24
To examine associations between the 24-h composition of movement behaviors (sedentary behavior [SB], light physical activity [LPA], moderate-to-vigorous physical activity [MVPA], and sleep) and physic Show more
To examine associations between the 24-h composition of movement behaviors (sedentary behavior [SB], light physical activity [LPA], moderate-to-vigorous physical activity [MVPA], and sleep) and physical and mental health in older adults using compositional data analysis. Data came from 4,150 adults aged ≥ 60 in the 2015 China Health and Nutrition Survey. Multiple‑balance isometric log‑ratio regression and compositional isotemporal substitution models were used to assess relative associations and the effect of time reallocation. The 24‑hour geometric mean composition was 43.1% sleep, 30.6% SB, 21.8% LPA, and 4.5% MVPA. LPA was positively associated with physical (β = 0.062, Replacing sedentary time or sleep with LPA, even in small amounts, is associated with better physical and mental health in older adults, supporting integrated 24‑hour activity guidelines that emphasize light‑intensity movement. Show less
📄 PDF DOI: 10.1186/s12877-026-07212-4
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
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
Zhouhua Li, Yuexiu Lei, Zheyu Wen +2 more · 2026 · Journal of affective disorders · Elsevier · added 2026-04-24
Physical activity (PA) is known to enhance brain health; however, prior research has predominantly concentrated on the total volume of PA, often overlooking the frequency of daily PA on an hourly basi Show more
Physical activity (PA) is known to enhance brain health; however, prior research has predominantly concentrated on the total volume of PA, often overlooking the frequency of daily PA on an hourly basis. This prospective cohort study examined 69,393 middle-aged and older adults, utilizing wrist-worn accelerometer data to assess PA. A novel PA frequency score was developed, which integrated light PA (LPA) and moderate-to-vigorous PA (MVPA) across 18 hourly segments (6:00 AM-12:00 AM). Participants were categorized into Inactive, Active, and Very Active groups. After adjusting for potential confounders, it was observed that individuals in the Active and Very Active groups exhibited a reduced risk of developing brain disorders such as dementia, anxiety, depression, migraine, Parkinson's disease, and stroke over a median follow-up period of 7.41 years. Magnetic Resonance Imaging (MRI) findings demonstrated that each unit increase in the PA frequency score correlated with a 51.55 mm Show less
no PDF DOI: 10.1016/j.jad.2026.121528
LPA
Shaowei Liu, Bin Ma, Yanju Liu +3 more · 2026 · BMC psychiatry · BioMed Central · added 2026-04-24
Non-suicidal self-injury (NSSI) is highly prevalent among adolescents with depression, yet the heterogeneity of underlying temperamental risk factors remains poorly understood. Traditional variable-ce Show more
Non-suicidal self-injury (NSSI) is highly prevalent among adolescents with depression, yet the heterogeneity of underlying temperamental risk factors remains poorly understood. Traditional variable-centered approaches fail to capture how distinct affective temperaments co-occur within individuals. This study aimed to identify latent profiles of affective temperaments and examine their association with NSSI, exploring the statistical mediating role of cognitive emotion regulation (CER). A cross-sectional study was conducted from February 2025 to September 2025 at the First Hospital of Hebei Medical University. A total of 290 adolescents (aged 10–19) diagnosed with Major Depressive Disorder were recruited, with 282 valid responses included in the final analysis. Participants completed the TEMPS-A, CERQ, and ASHS. Latent Profile Analysis (LPA) was utilized to identify temperament subgroups. Mediation analysis with bootstrapping was performed to test the indirect effects of CER strategies. LPA identified three distinct profiles: Resilient/Low-risk (Class 1, 32.6%), Anxious-Depressive (Class 2, 46.1%), and Mixed-Dysregulated (Class 3, 21.3%). The Mixed-Dysregulated group, characterized by simultaneous elevations in depressive, anxious, irritable, and cyclothymic temperaments, exhibited the highest frequency (45.2 ± 21.3 times/year) and prevalence (98.8%) of NSSI compared to other groups ( The findings delineate a specific “Mixed-Dysregulated” risk phenotype within adolescent depression that is associated with severe NSSI. Interventions should move beyond standard depression care to target cognitive flexibility and emotional regulation skills. Statistical mediation analysis suggests that this risk is mediated by maladaptive cognitive emotion regulation strategies. Not applicable. Show less
📄 PDF DOI: 10.1186/s12888-026-07910-8
LPA