👤 Jinman Li

🔍 Search 📋 Browse 🏷️ Tags ❤️ Favourites ➕ Add 🧪 BiometalDB 🧬 Extraction
3991
Articles
2551
Name variants
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
Hsiao-Hui Li, Po-Chun Chang, Yuan-Hsun Liao · 2026 · Scientific reports · Nature · added 2026-04-24
This paper presents the Assimilation Modified Emotional (AME) algorithm, which is an enhanced version of the traditional label propagation algorithm (LPA) designed to address key challenges in social Show more
This paper presents the Assimilation Modified Emotional (AME) algorithm, which is an enhanced version of the traditional label propagation algorithm (LPA) designed to address key challenges in social network analysis and emotional feature extraction. Traditional LPA methods, such as asynchronous label propagation and the Louvain algorithm, do not incorporate emotional representations and are often limited by local structural dependencies. The AME algorithm addresses these limitations by applying spectral algorithms, Markov chains, graph coarsening, and link prediction to simulate and optimize emotional transitions within the network. In addition, the AME algorithm enhances label representation through multi-label encoding, which allows for more accurate simulation of dynamic emotional states. Experimental results show that the AME algorithm achieves better performance than traditional LPA methods in terms of both accuracy and loss values. These findings indicate that the AME algorithm has strong potential for improving AI models used in social network analysis and emotional feature extraction. Show less
📄 PDF DOI: 10.1038/s41598-025-18482-0
LPA
Xiaozhao Lu, Ziyao Yuan, Xiaoyu Lin +13 more · 2026 · Diabetes, obesity & metabolism · Blackwell Publishing · added 2026-04-24
Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to Show more
Lipoprotein(a) [Lp(a)] and diabetes mellitus (DM) are independent risk factors for worse outcomes in coronary artery disease (CAD) patients. Evidence of their joint association is limited. We aimed to investigate the combined effect of elevated Lp(a) and DM on survival outcomes in CAD patients. This study included 65 547 CAD patients (62.6 ± 10.7 years, 27.7% female) from CIN-II and RED-CARPET cohorts. Patients were stratified into four groups by Lp(a) levels (< or ≥ 30 mg/dL) and DM status. Multivariable Cox regression models estimated associations with cardiovascular and all-cause mortality, examining additive and multiplicative interactions. During a median follow-up of 5.5 years, 10 686 (16.3%) patients died from all causes and 5106 (7.8%) died from cardiovascular causes. Patients with Lp(a) ≥ 30 mg/dL and DM were independently associated with cardiovascular mortality (adjusted hazard ratio [aHR]: 1.28, 95% CI: 1.20-1.35; aHR: 1.53, 95% CI: 1.44-1.62, all p < 0.001, respectively). Compared to patients with Lp(a) < 30 mg/dL without DM, the aHRs were 1.26 (95% CI: 1.16-1.36, p < 0.001), 1.51 (95% CI: 1.40-1.62, p < 0.001) and 2.00 (95% CI: 1.83-2.18, p < 0.001) for those with Lp(a) ≥ 30 mg/dL without DM, Lp(a) < 30 mg/dL with DM and Lp(a) ≥ 30 mg/dL with DM, respectively. Significant additive interaction between elevated Lp(a) and DM on cardiovascular mortality was observed, with 12% of the excess risk attributed. Similar associations were observed in all-cause mortality. In patients with CAD, elevated Lp(a) and DM act synergistically to increase the risk of cardiovascular and all-cause mortality, suggesting that both risks should be considered to integrate management. Show less
no PDF DOI: 10.1111/dom.70603
LPA
Yibo Zhang, Longying Tian, Ying Zhang +2 more · 2026 · BMC nursing · BioMed Central · added 2026-04-24
Patient safety competency (PSC) is a core element of nursing practice, essential for ensuring high-quality and safe patient care. Newly recruited nurses often face challenges such as transition shock, Show more
Patient safety competency (PSC) is a core element of nursing practice, essential for ensuring high-quality and safe patient care. Newly recruited nurses often face challenges such as transition shock, limited clinical experience, and fragmented safety education, which may hinder their ability to maintain patient safety. Most studies have assessed PSC using total scale scores, overlooking internal heterogeneity within this group. This study aimed to identify latent profiles of PSC among newly recruited nurses and explore the influencing factors to provide evidence for targeted competency development and management strategies. From July to August 2023, a convenience sample of newly recruited nurses was obtained from seven tertiary grade-A hospitals in Shandong Province, China. Data were collected using the General Information Questionnaire, the Transition Shock Scale of Newly Graduated Nurses, the Nurses' Perception of Organizational Support Scale, and the Patient Safety Nurse Competency Evaluation Scale. Latent Profile Analysis (LPA) was conducted to identify the potential subgroups of patient safety competency among newly recruited nurses. Univariate analysis and multivariate logistic regression were performed to examine the influencing factors associated with different latent profile categories. The patient safety competency of newly recruited nurses was categorized into 3 potential profiles: "high safety competency group" (36.9%), "medium safety competency group" (49.4%), and "low safety competency group" (13.7%). The results of the logistic regression analysis revealed that education level, average number of night shifts per week, participation in safety training, involvement in patient safety-related projects, transition shock, and perceived organizational support were significant predictors of patient safety competency among newly recruited nurses (P < 0.05). This study identified three distinct latent profiles of patient safety competency among newly recruited nurses, revealing a moderate overall competency level with notable heterogeneity. Nursing managers should pay particular attention to nurses with moderate and low competency levels and implement targeted, evidence-based interventions to strengthen their patient safety competency and promote safer clinical practice. Not applicable. Show less
no PDF DOI: 10.1186/s12912-026-04494-2
LPA
Miaomiao Chen, Shailing Ma, Xiaohui Liu +5 more · 2026 · Frontiers in reproductive health · Frontiers · added 2026-04-24
China's total fertility rate has reached a critically low level, dropping to approximately 1.0 by the end of 2023which is significantly below the population replacement level of 1.5. This decline refl Show more
China's total fertility rate has reached a critically low level, dropping to approximately 1.0 by the end of 2023which is significantly below the population replacement level of 1.5. This decline reflects a marked reduction in fertility intention among reproductive-aged women, exacerbating population aging and threatening long-term labor supply and social sustainability. Despite policy adjustments and governmental support initiatives, intended outcomes have not been realized. Current literature largely focuses on isolated determinants of fertility intention, overlooking heterogeneity within the population. Moreover, the pathways through which psychosocial factors operate across different subgroups remain poorly understood. Data for this study were derived from the 2021 Psychological and Behavioral Investigation of Chinese Residents (PBICR 2021), a nationally representative cross-sectional survey. Latent profile analysis (LPA) was employed to identify subtypes of fertility intention among reproductive-aged women, followed by multinomial logistic regression, which examined factors associated with different profiles. Among 2,973 reproductive-aged female participants, three distinct fertility intention profiles were identified via latent profile analysis: the Fertility Intention Decline Group (25.1%), the Low Fertility Intention Group (51.3%), and the High Fertility Intention Group (23.6%). Multinomial logistic regression analysis revealed that, compared with the Fertility Intention Decline Group, the Low Fertility Intention Group was significantly associated with family type, aged 20-40 years, residential location, having 2 children, and retirement status (all Fertility intention among reproductive-aged women demonstrates significant heterogeneity. This study identified three distinct latent profiles, each characterized by unique patterns of influencing factors. The findings highlight the necessity of moving beyond one-size-fits-all policy approaches and emphasize the importance of developing tailored interventions that account for the specific characteristics and determinants of each subgroup. Show less
📄 PDF DOI: 10.3389/frph.2026.1758039
LPA
Chong Liu, Nieran Lian, Kristin K Sznajder +3 more · 2026 · Journal of nursing management · added 2026-04-24
Nurses in traditional Chinese medicine (TCM) departments face significant sleep challenges associated with occupational stressors. However, person-centered analyses classifying these sleep patterns re Show more
Nurses in traditional Chinese medicine (TCM) departments face significant sleep challenges associated with occupational stressors. However, person-centered analyses classifying these sleep patterns remain scarce. This study aimed to identify heterogeneous sleep disturbance subgroups via latent profile analysis (LPA) and evaluate the performance of explainable machine learning models in discriminating these subgroups based on demographic and occupational features. A cross-sectional survey enrolled 7721 nurses from 130 TCM healthcare institutions in Liaoning Province (December 2024). Data encompassed demographic, occupational, and psychological variables obtained via self-administered questionnaires, including the Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance short form 8a. LPA was employed to categorize sleep disturbance patterns. Recursive feature elimination with random forest (RFE-RF) was used to select features associated with subgroup membership for five machine learning models. Models were trained on 70% of the data and evaluated on a 30% independent test set. The optimal classification model (XGBoost) underwent interpretability analysis using Shapley additive explanations (SHAP). LPA identified three subgroups: mild-stable (29.8%), moderate-fluctuating (60%), and severe-persistent (10.2%). Machine learning models achieved test AUCs of 0.71-0.84, with XGBoost demonstrating the highest discriminatory performance (AUC = 0.84, 95%CI: 0.83-0.85) in classifying subgroups. SHAP analysis indicated that monthly income, organizational support, hospital level, self-compassion, and resilience were the top five features contributing to the model's classification output. This study characterized three distinct sleep disturbance subgroups among TCM nurses, with the majority exhibiting moderate symptoms. The sequential application of LPA and explainable machine learning demonstrated robust performance in distinguishing sleep disturbance patterns. Identifying correlates-such as lower income and resilience-may assist nurse managers in stratifying risk and tailoring interventions for those most likely to fall into the severe subgroup. Future longitudinal studies are required to validate the stability of these subgroups and establish causal relationships. Show less
📄 PDF DOI: 10.1155/jonm/1269507
LPA
Yali Jiang, Chunyi Wang, Yangfan Hu +4 more · 2026 · Nursing in critical care · Blackwell Publishing · added 2026-04-24
Studies of surrogate decision-makers (SDMs) in the intensive care unit (ICU) often report high average levels of family decision-making self-efficacy (FDMSE). However, these findings contrast with the Show more
Studies of surrogate decision-makers (SDMs) in the intensive care unit (ICU) often report high average levels of family decision-making self-efficacy (FDMSE). However, these findings contrast with the significant decision conflict commonly observed in clinical practice. This discrepancy suggests that high aggregate FDMSE scores may mask underlying subgroups with distinct experiences. Identifying these latent profiles is essential for understanding the true experiences of ICU SDMs. This study aimed to identify distinct latent profiles of FDMSE among ICU SDMs and explore key influencing factors. A cross-sectional study was conducted among SDMs of ICU patients. Exploratory and confirmatory factor analysis (EFA/CFA) was performed to examine the factor structure of the Chinese FDMSE scale. The verified factor structure was then used for latent profile analysis (LPA). Lastly, univariate and multivariate analyses were performed to identify the main influencing factors. A total of 350 ICU SDMs were included in the analysis. The three-factor model, including treatment decision-making, comfort promotion decision-making, and facing death decision-making, provided a good fit for the Chinese FDMSE scale. Two profiles emerged: 'weak family decision-making self-efficacy', accounting for 55.9% of cases, and 'strong family decision-making self-efficacy', represented by the remaining 44.1%. The 'strong family decision-making self-efficacy' group was more likely to be observed in families where the patients held religious beliefs and were diagnosed with cancer, and where the family decision-makers held religious beliefs, had higher incomes, and had engaged in prior discussions about treatment preferences. This study verified the multi-dimensionality and heterogeneity of the FDMSE of ICU SDMs through EFA, CFA and LPA. The identification of a subgroup with low FDMSE differs from previous studies. Key modifiable factors include socio-economic resources, prior communication of the patients' preferences, and spiritual and cultural background, which serve as crucial levers for strengthening the decision-support framework in critical care settings. By identifying two distinct FDMSE profiles and key influencing factors, it offers critical care nurses a new perspective to design targeted interventions, thereby enhancing their ability to provide personalised decision support. Critical care nurses should receive structured end-of-life communication training to address the shared vulnerability of ICU SDMs in facing death decision-making self-efficacy across both profiles. Show less
no PDF DOI: 10.1111/nicc.70398
LPA
Bin Yang, Long Yin, Zongyu Yang +4 more · 2026 · Journal of exercise science and fitness · Elsevier · added 2026-04-24
This study aims to identify the 24-h movement behavior patterns of preschool children using Latent Profile Analysis based on Compositional Data Analysis (CoDA), and to examine their associations with Show more
This study aims to identify the 24-h movement behavior patterns of preschool children using Latent Profile Analysis based on Compositional Data Analysis (CoDA), and to examine their associations with physical fitness. The study employs a cross-sectional design. A total of 329 healthy children aged 4-6 years were selected. Accelerometers (ActiGraph wGT3-BT, Pensacola, FL, USA) were used to measure light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sedentary behavior (SB), while sleep was assessed through parent and teacher questionnaires. The assessment of physical fitness was conducted in accordance with the "Chinese National Physical Fitness Test Standards" (Preschooler Section). To address the multicollinearity problems among components of physical activity (PA), CoDA was first applied, subsequently, Latent Profile Analysis was utilized to categorize 24-h movement behavior patterns, while a Generalized Ordered Logit Model (GOLM) was applied to investigate their associations with physical fitness. Three distinct behavioral patterns emerged from the analysis: the "brown bear group" (moderate PA and SB, high SP, N = 176, 53.5%), the "cheetah group" (high PA/MVPA, low SB, moderate SP, N = 102, 31%), and the "koala group" (low PA, high SB, lower SP, N = 51, 15.5%). After adjusting for potential confounding factors, it was found that compared with the "koala group", the "brown bear group" and the "cheetah group" exhibited higher levels of physical fitness, with the probability of improving their physical fitness rating being 3.69 times and 6.36 times that of the "koala group," respectively. This study highlights the significant impact of active and healthy activity patterns on the physical fitness of preschool children, providing a foundation for formulating personalized preventive and interventional approaches in early childhood. Show less
📄 PDF DOI: 10.1016/j.jesf.2026.200459
LPA
Weiwei Xiang, Hua Ke, Xiaojia Song +10 more · 2026 · BMC women's health · BioMed Central · added 2026-04-24
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This stu Show more
This study aims to examine the health characteristics of female sex workers (FSWs) in entertainment venues and to investigate the relationship between these characteristics and sleep quality. This study employed a cross-sectional design and was conducted from January to April 2024 in Wuhan, China. Participants were FSWs recruited through snowball sampling from entertainment venues, including hotels, restaurants, nightclubs, karaoke bars and dance halls. Data were collected via structured questionnaires covering sociodemographic information, work experience, psychological stress, health status, sleep quality and circadian rhythms. Latent profile analysis (LPA) was employed to identify health characteristic profiles among FSWs, and multivariate logistic regression was used to examine the associations between these profiles and sleep quality. Among the 1,036 FSWs surveyed, 45.1% had poor sleep quality. LPA classified FSWs’ health characteristics into three profiles: the high overall functioning group, the lower physical–emotional functioning group and the lower psychosocial functioning group. Multivariate logistic regression analysis showed that FSWs in the lower physical–emotional functioning group had higher odds of poor sleep quality (OR = 2.184) compared with those in the high overall functioning group. FSWs in the lower psychosocial functioning group had substantially higher odds of poor sleep quality (OR = 7.755) than that in the high overall functioning group. FSWs demonstrate substantial heterogeneity in health characteristics and exhibit lower overall sleep quality compared with the general population. Psychological and physiological factors are major influencing factors for their sleep quality, suggesting the importance of prioritising mental and physical health in this population. Show less
📄 PDF DOI: 10.1186/s12905-026-04346-w
LPA
Yanjuan Ren, Jie Li, Xiaomin Lin +1 more · 2026 · Risk management and healthcare policy · added 2026-04-24
Reflective practice has emerged as a critical competency for psychiatric nurses, enabling them to critically evaluate and adapt their care approaches. Growing evidence suggests that reflective practic Show more
Reflective practice has emerged as a critical competency for psychiatric nurses, enabling them to critically evaluate and adapt their care approaches. Growing evidence suggests that reflective practice may serve as a key driver of high-quality caring behaviors, which are essential for establishing therapeutic relationships and improving outcomes in mental health settings. This study aimed to classify latent profiles of reflective practice among psychiatric nurses and examine their effects on caring behaviors. This cross-sectional study was conducted to recruit psychiatric nurses from ten mental health treatment centers across ten hospitals in Sichuan Province, China, between January and March 2024. Psychiatric nurses completed an online investigation encompassing the Reflective Practice Questionnaire and the Caring Behaviors Inventory (CBI). Latent profile analysis (LPA) and hierarchical regression analysis were employed to achieve the study objectives. A total of 346 psychiatry nurses were included in this study. The reflective practice of psychiatric nurses was classified into three subgroups in this study: "passive reflective participants" (n=48, 13.9%), "moderately balanced reflective practitioners" (n=175, 50.6%), and "high-achieving reflective leaders" (n=123, 35.5%). The hierarchical regression analysis revealed a significant positive association between distinct profiles of reflective practice and psychiatric nurses' caring behaviors (ΔR The identification of three distinct reflective practice profiles ("passive reflective participants", "moderately balanced reflective practitioners", and "high-achieving reflective leaders") provides a nuanced understanding of the reflective practice among psychiatry nurses. Targeted development programs, such as peer mentoring for the "passive" group and the "moderate" group, could be designed based on individual profile membership to optimize caring behaviors in psychiatric nursing. Show less
📄 PDF DOI: 10.2147/RMHP.S574450
LPA
Jia Pu, Lan Huang, Yuemei Li +1 more · 2026 · BMC women's health · BioMed Central · added 2026-04-24
This study investigated the latent profiles of reproductive concerns among women of childbearing age with systemic lupus erythematosus (SLE) and analyzed the differences in the characteristics across Show more
This study investigated the latent profiles of reproductive concerns among women of childbearing age with systemic lupus erythematosus (SLE) and analyzed the differences in the characteristics across these profiles. A questionnaire was administered to 332 female patients of childbearing age with SLE at four tertiary-grade general hospitals in Mianyang City, China. We used a general information questionnaire, the Reproductive Concerns After Cancer Scale (RCAC), the Medical Coping Modes Questionnaire (MCMQ), and the Social Support Rating Scale (SSRS). A latent profile analysis (LPA) and multiple logistic regression models were employed to investigate the characteristics of the latent profiles and the factors that influence reproductive concerns. The total score for the reproductive concerns among women with SLE of childbearing age was moderate (58.45 ± 13.51). Four latent profiles were identified: low reproductive concern–high infertility acceptance (12.66%), moderate reproductive concern–concern about personal health (18.95%), moderate reproductive concern–concern about the child’s health (45.64%), and high reproductive concern–balance (22.75%). The model fit indices that support the four latent profiles included high entropy (0.92) and a significant result of the Lo–Mendell–Rubin (LMR) adjusted likelihood ratio test ( The reproductive concerns observed among women of childbearing age with SLE exhibited significant heterogeneity. In the field of clinical nursing, personalized intervention measures should be developed based on distinct categorical characteristics and influencing factors to reduce reproductive concerns among members of this patient population. Show less
📄 PDF DOI: 10.1186/s12905-026-04342-0
LPA
Xin Li, BoWen Li · 2026 · Frontiers in public health · Frontiers · added 2026-04-24
Osteoporosis frequently affects older women and is strongly linked to their daily routines, which include both sedentary behavior (SEB) and physical activities (PA) of different intensities. This stud Show more
Osteoporosis frequently affects older women and is strongly linked to their daily routines, which include both sedentary behavior (SEB) and physical activities (PA) of different intensities. This study investigates the dose-response relationship of different SEB and PA patterns among community-dwelling older women and assesses the potential impact of time reallocation on osteoporosis risk through an isotemporal substitution analysis. In this study, 1,106 older women aged between 60 and 70 years in Tianjin participated. Their moderate to vigorous physical activity (MVPA), light physical activity (LPA), and SEB were objectively assessed using an accelerometer. The connection between MVPA, LPA, SEB, and osteoporosis was assessed using binary logistic regression models and isotemporal substitution models. The osteoporosis group and non-osteoporosis group comprised 461 and 645 subjects respectively, accounting for 41.68 and 58.32% of the total cohort. The osteoporosis group had significantly higher daily SEB compared to the non-osteoporosis group ( PA and SEB in older women exhibit a significant dose-response relationship with osteoporosis. Avoiding prolonged sitting and increasing PA duration both offer protective effects against osteoporosis in older women, with achieving a certain level of MVPA being the most effective protective measure. Show less
📄 PDF DOI: 10.3389/fpubh.2026.1717573
LPA
Yao Gao, Tao Dong, Ancha Baranova +9 more · 2026 · Molecular psychiatry · Nature · added 2026-04-24
Major depressive disorder (MDD) in adolescents is a critical public health concern, yet objective diagnostic biomarkers remain lacking. We conducted an integrative lipidomics study across human cohort Show more
Major depressive disorder (MDD) in adolescents is a critical public health concern, yet objective diagnostic biomarkers remain lacking. We conducted an integrative lipidomics study across human cohorts and a chronic unpredictable mild stress (CUMS) rat model. Targeted UPLC-MS/MS profiling was applied to a training cohort (95 MDD, 40 controls), and untargeted UPLC-HRMS profiling to an independent cohort (56 MDD, 37 controls). Candidate biomarkers were identified using univariate tests, partial least squares discriminant analysis, and three feature-selection methods (Boruta, LASSO, RFE), with predictive performance evaluated by cross-validation and external replication. Translational relevance was examined in CUMS rats through behavioral assays and lipidomic profiling of serum and brain tissues. Pathway enrichment and regression models explored metabolic context and clinical associations. In the training cohort, we found that 244 lipids were significantly altered, highlighting altered glycerophospholipid, glycerolipid, and sphingolipid metabolism. A 29-lipid panel achieved 90.4% cross-validation accuracy, while a reduced 7-lipid subset reached 94.8%. In the validation cohort, an 8-lipid panel achieved 71.2% accuracy, and a minimal 2-lipid set-LPA(18:2) and SPH(d16:1)-reached 72.1%. Cross-species analysis confirmed consistent downregulation of SPH(d16:1) in serum of both humans and rats, and of LPC(0:0/16:0) specifically in the rat prefrontal cortex. Regression analyses linked sex, age, and anxiety severity to lipid alterations. This cross-platform, cross-species study identifies reproducible lipid signatures of adolescent MDD, highlights SPH(d16:1) and LPC(0:0/16:0) as translational biomarkers, and implicates glycerophospholipid metabolism in MDD pathophysiology, providing a foundation for biomarker-guided diagnostics and therapeutics. Show less
📄 PDF DOI: 10.1038/s41380-026-03486-7
LPA
Ziliang Wu, Chen Qiu, Meimei Pan +6 more · 2026 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
Lipoprotein(a) [Lp(a)] has been recognized as a genetically determined and independent contributor to atherosclerotic cardiovascular disease. However, its role in lower extremity arterial disease (LEA Show more
Lipoprotein(a) [Lp(a)] has been recognized as a genetically determined and independent contributor to atherosclerotic cardiovascular disease. However, its role in lower extremity arterial disease (LEAD) among individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) remains insufficiently studied. Given the overlapping metabolic disturbances in both conditions, such as insulin resistance and lipid abnormalities, a potential relationship between Lp(a) and peripheral vascular injury in MASLD is biologically plausible. This study aimed to investigate the cross-sectional association between circulating Lp(a) concentrations and the presence of LEAD in a well-characterized MASLD population. A total of 468 MASLD patients undergoing routine health check-ups were included. Lp(a) levels were stratified into three categories: <10 mg/dL, 10–30 mg/dL, and ≥ 30 mg/dL. LEAD was diagnosed using duplex ultrasonography. Multivariable logistic regression models were used to assess the relationship between Lp(a) levels and the presence of LEAD, with adjustments for demographic variables, metabolic conditions, and lipid-related parameters. Subgroup analyses were conducted to assess potential effect modification. LEAD was diagnosed in 61.5% ( Elevated Lp(a) levels were associated with a higher prevalence of LEAD in patients with MASLD. Although the magnitude of association per unit increase was modest, higher Lp(a) concentrations were associated with greater LEAD prevalence. These findings should be interpreted cautiously and viewed as hypothesis-generating, particularly with respect to subgroup analyses. Prospective studies are needed to clarify causality and clinical relevance. The online version contains supplementary material available at 10.1186/s12872-026-05600-7. Show less
📄 PDF DOI: 10.1186/s12872-026-05600-7
LPA
Xiaoxiao Li, Yanyan Jiao, Zhongqiang Guo +4 more · 2026 · Acta psychologica · Elsevier · added 2026-04-24
This study employed a latent profile analysis (LPA) to identify distinct subgroups of learned helplessness among Chinese breast cancer chemotherapy patients and examined influencing factors. Through c Show more
This study employed a latent profile analysis (LPA) to identify distinct subgroups of learned helplessness among Chinese breast cancer chemotherapy patients and examined influencing factors. Through convenience sampling, 260 breast cancer chemotherapy patients aged 18-74 years from a tertiary hospital in Henan Province were recruited between May 2024 and January 2025. Data were collected using a general demographic questionnaire, the Learned Helplessness Scale, the Brief Illness Perception Questionnaire, the Social Support Rating Scale, and the General Self-Efficacy Scale. An LPA was applied to classify learned helplessness patterns, followed by a multivariate logistic regression to determine the influencing factors. The latent profile analysis revealed three distinct profiles of learned helplessness among breast cancer patients undergoing chemotherapy: a "low helplessness-low hopelessness stable profile" (17.0%), a "moderate helplessness-moderate hopelessness fluctuating profile" (52.0%), and a "high helplessness-high hopelessness profile" (31.0%). The multivariable logistic regression revealed that age range 18-44 years, low monthly household income per capita, fatigue, and illness perception were significantly associated with the "high helplessness-high hopelessness profile" (P < 0.05). Conversely, the age range 45-59 years was significantly associated with the "moderate helplessness-moderate hopelessness fluctuating profile" (P < 0.001). Furthermore, experiencing ≤2 chemotherapy-related side effects, a higher level of perceived social support, and greater self-efficacy were significant predictors of membership in the "low helplessness-low hopelessness profile" (P < 0.05). Breast cancer chemotherapy patients were categorized into three distinct subgroups, which were influenced by age, income, fatigue, treatment side effects, illness perception, self-efficacy, and social support. Show less
no PDF DOI: 10.1016/j.actpsy.2026.106392
LPA
Xinyan Li, Zhongsu Wang, Juan Liang +3 more · 2026 · Journal of cardiovascular pharmacology · added 2026-04-24
Lipoprotein(a) [Lp(a)] is a genetically determined independent risk factor for atherosclerotic cardiovascular disease (ASCVD) that drives a significant residual risk through proatherogenic, proinflamm Show more
Lipoprotein(a) [Lp(a)] is a genetically determined independent risk factor for atherosclerotic cardiovascular disease (ASCVD) that drives a significant residual risk through proatherogenic, proinflammatory, and prothrombotic pathways. However, current mainstay lipid-lowering therapies such as statins have limited efficacy in reducing Lp(a) levels, highlighting a critical therapeutic gap. This review aims to synthesize evidence on the role of Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) inhibitors in targeting Lp(a). We systematically searched PubMed and Embase for clinical trials and mechanistic studies (2010-2025), using the PRISMA and AMSTAR-2 frameworks to ensure methodological rigor and demonstrated that PCSK9 inhibitors (eg, alirocumab, evolocumab, and tafolecimab) not only reduced low-density lipoprotein (LDL-C) by 55%-60% but also lowered Lp(a) by 20%-30%. The efficacy of these agents varies ethnically, with tafolecimab showing superior performance in East Asian populations, which is partly attributable to the higher prevalence of the PCSK9 R46L loss-of-function allele. Mechanistically, PCSK9 inhibitors lowered Lp(a) levels through 2 pathways: suppression of hepatic synthesis and enhanced plasma clearance. This evidence supports the 2023 ESC guidelines, which issued a Class IIa recommendation for PCSK9 inhibitor use in patients with ASCVD and elevated Lp(a) levels. Given the evolving landscape, further research is warranted to confirm the role of these therapies in precision medicine paradigms for managing Lp(a)-associated risks. Show less
no PDF DOI: 10.1097/FJC.0000000000001794
LPA
Chenlin Li, Yanping Qiu, Nan Zheng +3 more · 2026 · BMC public health · BioMed Central · added 2026-04-24
This cross-sectional study aimed to examine the associations between the 24-h movement behaviors and mental health among university students in China, and to determine the optimal behavioral balance b Show more
This cross-sectional study aimed to examine the associations between the 24-h movement behaviors and mental health among university students in China, and to determine the optimal behavioral balance based on the top 5% of model-predicted mental health outcomes using compositional data analysis. A total of 6,084 university students aged 17–24 years in Southwest China self-reported their daily durations of moderate-to-vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SED), and sleep (SLP). They were stratified by gender and then randomly and equally assigned to the “recommendation” group and the “validation” group. Using compositional data analysis, time-use compositions (MVPA, LPA, SED, SLP) were transformed into isometric log-ratios (with quadratic terms as needed) and subsequently used in regression models to predict the three mental health outcomes. All possible combinations of motion components were examined to determine the combination with the highest correlation (top 5%) for each outcome. Through research and analysis of the recommendation groups, the optimal combination of average (range) time usage is determined as follows: for males, MVPA 92 (60–110) min/day, LPA 361 (310–400) min/day, SED 372 (350–480) min/day, SLP 614 (530–680) min/day; for females, MVPA 58 (40–90) min/day, LPA 290 (180–390) min/day, SED 445 min (400–560), SLP 665 (580–740) min/day. The recommended durations served as benchmarks for the validation group. Participants who met the optimal 24-h movement behavior time showed significantly lower depression (males: β = –1.290, The optimal 24-h movement behavior time differs between men and women. Males tend to require a longer optimal MVPA duration than females, while females require a longer optimal SLP duration than males. The findings provide valuable reference for developing 24-h movement guidelines and promoting healthy and balanced lifestyles among university students. [Image: see text] The online version contains supplementary material available at 10.1186/s12889-026-26534-x. Show less
📄 PDF DOI: 10.1186/s12889-026-26534-x
LPA
Muhammad Suliman, Hongqun Liu, Xinyi Liu +4 more · 2026 · Journal of cancer survivorship : research and practice · Springer · added 2026-04-24
Depression is prevalent among colorectal cancer (CRC) survivors. Although various physical activity intensities are differentially associated with depressive symptoms, the underlying mediator and mode Show more
Depression is prevalent among colorectal cancer (CRC) survivors. Although various physical activity intensities are differentially associated with depressive symptoms, the underlying mediator and moderator involving interoception and mindfulness, remain unclear. This study aims to examine whether interoceptive accuracy differentially mediates the relationship between various physical activity intensities and depressive symptoms and whether mindfulness moderates these pathways. In this multicenter cross-sectional study, 395 CRC survivors completed validated questionnaires assessing depressive symptoms, physical activity participation, interoceptive accuracy, and mindfulness. Mediation and moderated mediation analyses via PROCESS version 4.1 for SPSS tested whether interoceptive accuracy mediated associations between light and moderate-to-vigorous physical activity (LPA vs. MVPA) and depressive symptoms, and whether mindfulness moderated these pathways. Both LPA and MVPA are negatively associated with depressive symptoms (p < 0.001). Interoceptive accuracy significantly mediated these associations, accounting for 49.09% of the total effect for LPA and 20.56% for MVPA. Mindfulness moderated the LPA-interoceptive accuracy (B = -0.004, p = 0.031), interoceptive accuracy-depression (B = -0.022, p = 0.004), and MVPA-depression pathways (B = -0.001, p = 0.034), suggesting differential, intensity-dependent associations. LPA showed negative associations with depressive symptoms, with interoceptive accuracy fully mediating this association. In contrast, MVPA demonstrated both direct and indirect associations with depressive symptoms, partially mediated by interoceptive accuracy. Mindfulness strengthened these relationships through complementary and synergistic moderation, highlighting the dynamic interaction between bodily awareness and physical activity in psychological recovery. Tailoring gentle, mindful movement to enhance interoception may offer a feasible, integrative rehabilitation strategy to reduce depression among CRC survivors. Show less
📄 PDF DOI: 10.1007/s11764-026-01979-6
LPA
Luyi Xu, Tingting Lin, Zheng Wang +3 more · 2026 · BMC geriatrics · BioMed Central · added 2026-04-24
This study aimed to identify the heterogeneity of attitudes toward ageing among older adults in the “early transition period” (the initial 2–4 weeks after nursing homes transition from home to nursing Show more
This study aimed to identify the heterogeneity of attitudes toward ageing among older adults in the “early transition period” (the initial 2–4 weeks after nursing homes transition from home to nursing homes). and the mediation effect of self-efficacy between attitudes toward ageing and quality of life (QoL). A total of 300 older adults were enrolled from October 2023 to May 2024. Participants completed the General Information Questionnaire, the Attitudes to Ageing Questionnaire (AAQ), the World Health Organization Quality of Life-Brief (WHOQOL-BREF), and the General Self-Efficacy Scale (GSES). Latent profile analysis (LPA), R3STEP methods, BCH methods, and mediation analysis were conducted to analyze the data. LPA categorized the attitudes toward ageing into three profiles: most negative (18.333%), moderately negative (64.000%), and positive (17.667%). Attitudes toward ageing profiles were associated with the following factors: age, pension, number of children, number of chronic diseases, ADL, willingness to reside in nursing homes, and social isolation. Self-efficacy partially mediates between attitudes toward ageing and the three dimensions of QoL (physical health, psychological health, and environmental health). Older adults during the “early transition period” had negative attitudes toward ageing. It may be related to the Chinese traditional interpersonal communication mode, family culture, and various maladaptive problems. Older adults who have two or more children, chronic diseases, no pension, moderate to severe dependency, involuntary admission to nursing homes, and social isolation are associated with more negative attitudes toward ageing. Mediation analysis reminds that self-efficacy can be used as intervention targets to improve the QoL. The online version contains supplementary material available at 10.1186/s12877-026-07007-7. Show less
📄 PDF DOI: 10.1186/s12877-026-07007-7
LPA
Zhi-Wei Li, Bei-Hao Shi, Jie Ren +4 more · 2026 · Frontiers in medicine · Frontiers · added 2026-04-24
Peripheral artery disease (PAD) is a major manifestation of systemic atherosclerosis and affects vascular health in older adults. Dyslipidaemia contributes significantly to PAD, but the predictive val Show more
Peripheral artery disease (PAD) is a major manifestation of systemic atherosclerosis and affects vascular health in older adults. Dyslipidaemia contributes significantly to PAD, but the predictive value of composite lipid indices remains unclear. The non-high-density lipoprotein cholesterol (non-HDL-C) to high-density lipoprotein cholesterol (HDL-C) ratio (NHHR) reflects the balance between atherogenic and protective lipoproteins. This study aimed to explore the association between the NHHR and PAD among vascular surgery inpatients aged ≥50 years in Kunshan, China. This retrospective cross-sectional study included 3,532 patients (aged ≥ 50 years) hospitalized at the Affiliated Kunshan Hospital of Jiangsu University, Suzhou, from December 2017 to August 2024. NHHR, calculated as (total cholesterol - HDL-C)/HDL-C, was the exposure variable; PAD, defined as PAD-like symptoms with an ankle brachial index < 0.9, was the outcome. Covariates included age, sex, lipoprotein(a) level [Lp(a)], apolipoprotein A1 level (Apo A1), alanine aminotransferase (ALT) level, neutrophil count (NEUT), hypertension status, diabetes status, smoking status, and alcohol consumption status. Multivariate logistic regression, smooth curve fitting, and threshold analyses were performed. After adjustment for confounders, the NHHR was nonlinearly associated with PAD (OR = 0.77; 95% CI: 0.65-0.93; The NHHR was associated with the presence of PAD, with the evidence suggesting a nonlinear relationship and potential sex-specific differences. Given the retrospective cross-sectional design, this association does not support causal inference or strong predictive claims. The NHHR may help identify individuals who could benefit from further clinical evaluation for PAD, but prospective studies are needed to confirm its clinical relevance before its routine application. Show less
📄 PDF DOI: 10.3389/fmed.2026.1739515
LPA
Qingyu Wang, Meijing Zhou, Sha Li +4 more · 2026 · Journal of nursing management · added 2026-04-24
To investigate potential types of food avoidance among patients with inflammatory bowel disease (IBD) and identify the contributing factors. Food avoidance may be an important risk factor for poor phy Show more
To investigate potential types of food avoidance among patients with inflammatory bowel disease (IBD) and identify the contributing factors. Food avoidance may be an important risk factor for poor physical and mental health in patients with IBD. However, there is limited research on food avoidance within the Chinese context. Between July 2022 and December 2023, patients with IBD during appointment at the First Affiliated Hospital with Nanjing Medical University was investigated with paper questionnaires to assess food avoidance, food category avoidance, fear of disease progression, negative illness perception, IBD-related self-efficacy, and social support. Demographic and disease-related characteristics were also collected. Latent profile analysis (LPA) was used to examine food avoidance in patients with IBD, and the correlates were investigated using regression analysis. LPA showed that respondents could be classified into three groups in terms of food avoidance, namely, the mild-food avoidance adaptation group ( Patients with IBD may exhibit long-term, spontaneous food avoidance, which often presents at high levels. Furthermore, patients with IBD exhibit considerable heterogeneity in their food avoidance patterns, categorizing them into three distinct categories. Future dietary management strategies should be tailored based on the specific characteristics and predictive factors of these food avoidance patterns. Given the prevalence and heterogeneity of food avoidance in patients with IBD, nurse managers should implement stratified interventions tailored to patient characteristics. Training nurses in culturally sensitive dietary education and emotional regulation strategies may improve the management of food-related behaviors and support patients' adaptive coping with the disease. Show less
📄 PDF DOI: 10.1155/jonm/3669996
LPA
Dan Jiang, Yi-Ling Liu, Jian Liu +7 more · 2026 · Lipids in health and disease · BioMed Central · added 2026-04-24
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limite Show more
Calcific aortic valve disease (CAVD) is a cardiovascular disease closely associated with aging. The role of lipoprotein(a) [Lp(a)] has attracted considerable attention in recent years. However, limited research has simultaneously explored the relationships between Lp(a), age, and CAVD. This study sought to assess the relationship linking Lp(a), time-weighted Lp(a), and CAVD. A total of 5,156 inpatients with comprehensive clinical data were recruited for this study. The associations of Lp(a) and time-weighted Lp(a) with CAVD were examined via multivariate logistic regression analysis, alongside the application of restricted cubic spline analysis. The diagnostic utility of Lp(a) and time-weighted Lp(a) for CAVD was assessed by constructing receiver operating characteristic (ROC) curves. CAVD prevalence rose with age, whereas the rate of increase diminished with advancing age. The average Lp(a) level in the young populations with CAVD was more than twice that in the No-CAVD group, particularly among those aged 55 years or younger. The prevalence of CAVD in non-elderly populations was markedly 2–4 fold greater in the higher Lp(a) group (> 30 mg/dL) than in the lower Lp(a) group (≤ 30 mg/dL). Multivariate adjusted odds ratios ‌(ORs) for CAVD increased with advancing Lp(a) or age. Time-weighted Lp(a), which takes into account both age and Lp(a), was more strongly linked to elevated CAVD risk than Lp(a) alone. Time-weighted Lp(a) enhanced the diagnostic value of CAVD, improving both sensitivity and specificity. The risk of CAVD is strongly associated with both age and elevated Lp(a) levels. Time-weighted Lp(a), which integrates these factors, serves as a superior indicator that better captures cumulative long-term Lp(a) variation and yields stronger CAVD risk stratification. The online version contains supplementary material available at 10.1186/s12944-026-02884-8. Show less
📄 PDF DOI: 10.1186/s12944-026-02884-8
LPA
Tingting Li, Lin Wang, Wenyu Li +3 more · 2026 · Angiology · SAGE Publications · added 2026-04-24
The present study aimed to investigate the combined impact of lipoprotein (a) [Lp(a)] and low-density lipoprotein (LDL) subfractions on cardiovascular outcomes in patients with acute coronary syndrome Show more
The present study aimed to investigate the combined impact of lipoprotein (a) [Lp(a)] and low-density lipoprotein (LDL) subfractions on cardiovascular outcomes in patients with acute coronary syndrome (ACS). The study enrolled 2061 ACS patients from Tianjin Chest Hospital. Participants were categorized into 4 groups based on their Lp(a) and the concentration of the sixth component particles of LDL(LDL-P6). The primary endpoint was the occurrence of major adverse cardiovascular events (MACE). The relationship between LDL-P6, Lp(a), and MACE was evaluated. Over a mean follow-up period of 5.4 years, 456 (22.1%) patients experienced MACE. Multivariate analysis identified both LDL-P6 and Lp(a) as significant independent predictors of MACE in ACS patients. Those in the highest-risk group had a substantially higher incidence of MACE compared with the lowest-risk group (HR 5.718; 95% CI 3.703-8.829; Show less
no PDF DOI: 10.1177/00033197251415207
LPA
Yanghong Zou, Chunhai Zhang, Hui Bian +5 more · 2026 · International immunopharmacology · Elsevier · added 2026-04-24
The abuse of methamphetamine (METH) is associated with an increased risk of Parkinson's disease (PD), whereas microglial polarization and glucose metabolism disorders are closely related to the progre Show more
The abuse of methamphetamine (METH) is associated with an increased risk of Parkinson's disease (PD), whereas microglial polarization and glucose metabolism disorders are closely related to the progression of PD. This study aimed to investigate the specific molecular mechanism underlying the promotion of PD progression by METH through the regulation of microglial polarization and glycolysis. METH-induced C57BL/6 mice and BV2 cells were used to construct PD-like neurotoxicity animal and cell models for experimental investigation. Behavioral tests, immunohistochemistry and Nissl staining were used to assess the behavioral ability and neuronal damage of the animals. The levels of related proteins, inflammatory cytokines and glycolysis were detected using immunofluorescence, ELISA, Western blotting, and CCK-8 assays. METH treatment significantly promoted behavioral disorders in PD mice, reduced the number of TH-positive neurons, and aggravated neuronal damage in the substantia nigra (SN). In addition, METH decreased the M2 marker proteins Arg-1 and CD206 and increased the M1 marker proteins iNOS and CD86; the proinflammatory cytokines TNF-α, IL-β, and IL-6; and glucose uptake, glucose consumption and lactic acid production, thus promoting M1 polarization and glycolytic activity in BV2 cells. In terms of the underlying molecular mechanism, METH treatment significantly increased the level of LPA. METH promotes LPA expression via upregulation of LIPH expression, and activates the PI3K/AKT pathway. Knockdown of LIPH or treatment with BrP-LPA reduces the ability of METH to promote M1 microglial polarization and glycolytic activity. Furthermore, the addition of the PI3K/AKT signaling pathway activator 740 YP weakened the inhibitory effect of BrP-LPA on the above process. METH may promote M1 polarization and glycolytic activity in microglia by activating LIPH/LPA/PI3K/AKT signaling, thus promoting the progression of PD. Show less
no PDF DOI: 10.1016/j.intimp.2026.116306
LPA
Alexander C Razavi, Omar Dzaye, Harpreet S Bhatia +18 more · 2026 · JACC. Cardiovascular imaging · Elsevier · added 2026-04-24
no PDF DOI: 10.1016/j.jcmg.2025.12.008
LPA
Yali Jiang, Juanjuan Zhao, Kun Li +10 more · 2026 · BMC medical education · BioMed Central · added 2026-04-24
Massive open online courses (MOOCs) have transformed global education, yet their long-term effectiveness and evolving learner engagement remain underexplored. This study aims to comprehensively evalua Show more
Massive open online courses (MOOCs) have transformed global education, yet their long-term effectiveness and evolving learner engagement remain underexplored. This study aims to comprehensively evaluate a nursing MOOC over six years, examining learner engagement, identifying distinct learner profiles, and assessing changes across different developmental stages to inform future MOOC design. A retrospective study was conducted on 4171 completers of the Medical Nursing MOOC on a Chinese MOOC platform, covering eleven semesters from 2018 to 2023. Latent profile analysis (LPA) categorized learners based on unit test scores, and profile distributions were compared across the MOOC's developmental stages. The Medical Nursing MOOC attracted 69,642 registrants with a 5.99% completion rate. Among the 4171 individuals who completed the course, latent profile analysis identified six distinct learner types, demonstrating significant differences in overall learning effect (H = 2823.604, P < 0.001). The chi-squared analysis revealed significant differences between the proportions of the six profiles regarding MOOC developmental stages (χ Findings highlight the evolving role of MOOCs in nursing education. Despite challenges in long-term engagement, the increasing proportion of highly engaged learners and declining dropout rates indicate growing effectiveness and sustainability. These insights provide evidence-based guidance for optimizing MOOC design and implementation. Show less
📄 PDF DOI: 10.1186/s12909-026-08679-w
LPA
Sitian Liu, Junnan Lin, Jishun Jiang +3 more · 2026 · International journal of molecular sciences · MDPI · added 2026-04-24
Dichondra (
📄 PDF DOI: 10.3390/ijms27021009
LPA
Miao Yu, Libin Yao, Sanjeev Shahi +12 more · 2026 · Radiology · added 2026-04-24
Background Although substantial evidence has demonstrated the impact of obesity on brain structure and cognition, the heterogeneity of adiposity-particularly in terms of fat distribution patterns-and Show more
Background Although substantial evidence has demonstrated the impact of obesity on brain structure and cognition, the heterogeneity of adiposity-particularly in terms of fat distribution patterns-and its differential neurologic effects remain poorly understood. Purpose To identify body fat distribution patterns with MRI and latent profile analysis (LPA) and their associations with brain structure measurements, cognition, and neurologic diseases. Materials and Methods This secondary analysis used prospective data from the UK Biobank, including health records and MRI scans of the brain, heart, and abdomen. Fat distribution profiles were classified using LPA based on eight body mass index (BMI)-adjusted MRI-derived fat quantification metrics. Differences in brain volume, white matter properties, cognition, and the risk of neurologic disorders were analyzed across profiles and relative to a benchmark lean profile; analyses were stratified by sex. Group differences were examined using analysis of covariance (ANCOVA) or rank-based ANCOVA. Results Among 25 997 participants (mean age, 55 years ± 7.4 [SD]; 13 536 female participants), LPA identified six profiles of body fat distribution in both sexes. Four high-adiposity patterns were identified, including the pancreatic-predominant profile (profile 1), with elevated proton density fat fraction (mean BMI-adjusted Show less
no PDF DOI: 10.1148/radiol.252610
LPA
Xiao Liang, Raffy C F Chan, Justin A Haegele +8 more · 2026 · Research in developmental disabilities · Elsevier · added 2026-04-24
Physical inactivity is a health concern for children and adolescents with neurodevelopmental disorders (NDDs) as it directly increases their risk of developing various health problems. Evidence on dif Show more
Physical inactivity is a health concern for children and adolescents with neurodevelopmental disorders (NDDs) as it directly increases their risk of developing various health problems. Evidence on differences in accelerometer-assessed physical activity between children and adolescents with and without NDDs is inconclusive. And age- and body mass index (BMI)-related effects on physical activity remain unclear. The systematic literature searches were performed in 6 databases up to March 2025. Methodological quality was evaluated by the Newcastle-Ottawa Scales. Data were pooled using a random-effects model. Hedges' g was used to express the effect size index with 95 % confidence interval (CI). Meta-regression on age and BMI was also performed to investigate the potential moderating effects. Out of the 2167 studies initially identified, 28 were included in the analysis, which comprised total physical activity (TPA), moderate-to-vigorous physical activity (MVPA), and light physical activity (LPA) included in the meta-analysis, respectively. These studies involved 1060 children and adolescents with NDDs and 1820 without, aged 6.6-16.9 years. A small-to-moderate effect size exists for the difference in TPA (g=-0.299) and MVPA (g=-0.479) between children and adolescents with and without NDD, particularly indicating a difference in 12.7 min of MVPA daily. The difference in LPA was not significant (g=0.450, p = 0.125). The decline in MVPA with age was more pronounced in those with NDDs, and the difference in MVPA was smaller for those with lower BMI. The variation in MVPA differences by age and BMI highlights the need to develop better physical activity habits and reduce these disparities for children and adolescents with NDDs. Show less
no PDF DOI: 10.1016/j.ridd.2026.105233
LPA
Guogang Xin, Jiaqian Xu, Ling Jiang +5 more · 2026 · BMC psychology · BioMed Central · added 2026-04-24
Improved internet access has exposed rural adolescents in China to a greater risk of internet addiction. However, existing studies seldom examine the relationship between dynamic changes in internet a Show more
Improved internet access has exposed rural adolescents in China to a greater risk of internet addiction. However, existing studies seldom examine the relationship between dynamic changes in internet addiction and psychosocial maladjustment. This study aims to explore the transition patterns of internet addiction and its associations with emotional and interpersonal problems over time. A one-year longitudinal survey was conducted among 782 middle school students in rural China. Latent Profile Analysis (LPA) was conducted to identify internet addiction profiles at two time points. Latent Profile Transition Analysis (LPTA) was then used to examine the transition patterns between profiles over time. Subsequently, statistical analyses were conducted to explore how these transitions were associated with emotional and interpersonal problems. Three profiles of internet addiction were identified: minimal-internet addiction, low-internet addiction, and high-internet addiction. Based on LPTA, most adolescents with higher internet addiction at T1 shifted to lower-severity profiles over time (high → minimal: 35.3%; low → minimal: 39.8%; high → low: 33.3%), while some with initially lower levels transitioned to more severe profiles (minimal → high: 6.9%; low → high: 12.2%; minimal → low: 25.7%). Transition into higher addiction profiles predicted increased depression, anxiety, and poorer relationships with parents, peers, and teachers. Conversely, reductions in addiction were linked to improved depressive symptoms. Changes in internet addiction have an impact on adolescent psychosocial maladjustment. Early detection and flexible interventions are essential in rural settings. Show less
📄 PDF DOI: 10.1186/s40359-026-03992-x
LPA
Malachy Bishop, Jian Li · 2026 · Work (Reading, Mass.) · SAGE Publications · added 2026-04-24
BackgroundMultiple sclerosis (MS) is a prevalent, frequently progressive condition of the central nervous system that can significantly affect employment and career participation. Although researchers Show more
BackgroundMultiple sclerosis (MS) is a prevalent, frequently progressive condition of the central nervous system that can significantly affect employment and career participation. Although researchers have extensively catalogued the factors that people with MS face in maintaining employment, the priorities of people working with MS in terms of career resources and information needs have not been extensively evaluated.ObjectiveWe sought (a) to identify the types of career information and resources that employed or recently-employed people with MS prioritize, and (b) to assess the extent to which the need for these career resources may vary among identifiable subgroups.MethodDescriptive statistics and latent profile analysis (LPA) were applied to the responses of 376 iConquerMS members who were either employed ( Show less
no PDF DOI: 10.1177/10519815251413174
LPA