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1. Application of a deep learning algorithm for the diagnosis of HCC

Author

Yu, Philip Leung Ho, Chiu, Keith Wan-Hang, Lu, Jianliang, Lui, Gilbert C.S., Zhou, Jian, Cheng, Ho-Ming, Mao, Xianhua, Wu, Juan, Shen, Xin-Ping, Kwok, King Ming, Kan, Wai Kuen, Ho, Y.C., Chan, Hung Tat, Xiao, Peng, Mak, Lung-Yi, Tsui, Vivien W.M., Hui, Cynthia, Lam, Pui Mei, Deng, Zijie, Guo, Jiaqi, Ni, Li, Huang, Jinhua, Yu, Sarah, Peng, Chengzhi, Li, Wai Keung, Yuen, Man-Fung, and Seto, Wai-Kay

Published
2025
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5. Cigarette Smoking Is Associated With Lower Chance of Hepatitis B Surface Antigen Seroclearance and Altered Host Immunity.

Author

Kwok, Tsz‐Yan, Hui, Rex Wan‐Hin, Mao, XianHua, Ling, Guang‐Sheng, Wong, Danny Ka‐Ho, Huang, Fung‐Yu, Fung, James, Seto, Wai‐Kay, Yuen, Man‐Fung, and Mak, Lung‐Yi

Subjects
MONONUCLEAR leukocytes, HEPATITIS associated antigen, REGULATORY T cells, TRANSFORMING growth factors-beta, CHRONIC hepatitis B, T cells
Abstract

Cigarette smoking is associated with worse clinical outcomes in patients with chronic hepatitis B (CHB) infection, but the effects on hepatitis B surface antigen (HBsAg) seroclearance are unclear. This study aimed to investigate the effect of active smoking on HBsAg seroclearance (SC) and its impact on peripheral blood lymphocytes in patients with CHB infection. Longitudinal follow‐up data was retrieved in 7833 antiviral‐treated CHB subjects identified from a centralised electronic patient record database (Part 1). Phenotypic analysis of peripheral blood mononuclear cells (PBMCs) from 27 CHB‐infected patients (6 active smokers; 13 with SC) was performed by flow cytometry to assess programmed death‐1 (PD‐1) expression and proportion of regulatory T cells (CD4+CD25+CD127lo). Effector function of HBV‐specific T cells was examined by comparing granzyme B (GZMB) and transforming growth factor beta (TGFβ) production in undepleted PBMCs and Treg‐depleted PBMCs after 7 days in vitro stimulation with HBV envelope protein overlapping peptides (Part 2). Over a median follow‐up of 5 years, smoking was associated with lower probability of SC (aHR 0.70, 95% CI 0.57–0.87). PD‐1 expression was increased in CD4+ T cells, CD8+ T cells and CD20+ B cells among smokers compared to non‐smokers and positively correlated with pack years (all p < 0.05). Treg depletion led to partial functional recovery of HBV‐specific T cells, with significantly bigger magnitude in smokers (p = 0.0451, mean difference = 4.68%) than non‐smokers (p = 0.012, mean difference = 4.2%). Cigarette smoking is associated with lower chance of HBsAg seroclearance, higher PD‐1 expression on lymphocytes, and impairment of effector functions of HBV‐specific T cells in CHB. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Glycaemic control is a modifiable risk factor for hepatocellular carcinoma and liver‐related mortality in patients with diabetes.

Author

Mao, Xianhua, Cheung, Ka‐Shing, Tan, Jing‐Tong, Mak, Lung‐Yi, Lee, Chi‐Ho, Chiang, Chi‐Leung, Cheng, Ho‐Ming, Hui, Rex Wan‐Hin, Leung, Wai K., Yuen, Man‐Fung, and Seto, Wai‐Kay

Subjects
*GLYCEMIC control, *PROPENSITY score matching, *FATTY liver, *DIABETES, *HEPATOCELLULAR carcinoma
Abstract

Summary: Background: Optimal glycaemic control has well‐established health benefits in patients with diabetes mellitus (DM). It is uncertain whether optimal glycaemic control can benefit liver‐related outcomes. Aims: To examine the association of optimal glycaemic control with hepatocellular carcinoma (HCC) and liver‐related mortality. Methods: In a population‐based cohort, we identified patients with newly diagnosed DM between 2001 and 2016 in Hong Kong. Optimal glycaemic control was defined as mean haemoglobin A1c (HbA1c) <7% during the 3‐year lead‐in period after DM diagnosis. By applying propensity score matching to balance covariates, we analysed glycaemic control via competing risk models with outcomes of interest being HCC and liver‐related mortality. Results: We identified 146,430 patients (52.2% males, mean age 61.4 ± 11.8 years). During a median follow‐up duration of 7.0 years, 1099 (0.8%) and 978 (0.7%) patients developed HCC and liver‐related deaths. Optimal glycaemic control, when compared to suboptimal glycaemic control, was associated with reduced risk of HCC (subdistribution hazard ratio [SHR] 0.70, 95% CI 0.61–0.79). The risk of HCC increased with incremental HbA1c increases beyond >7% (SHR 1.29–1.71). Significant associations with HCC were also found irrespective of age (SHR 0.54–0.80), sex (SHR 0.68–0.69), BMI <25 or ≥25 kg/m2 (SHR 0.63–0.75), smoking (SHR 0.61–0.72), hepatic steatosis (SHR 0.67–0.68) and aspirin/statin/metformin use (SHR 0.67–0.75). A lower risk of liver‐related mortality in relation to optimal glycaemic control was also observed (SHR 0.70, 95% CI 0.61–0.80). Conclusions: Glycaemic control is an independent risk factor for HCC and liver‐related mortality, and should be incorporated into oncoprotective strategies in the general DM population. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Effect of metabolic dysfunction‐associated steatotic liver disease on BNT162b2 immunogenicity against the severe acute respiratory syndrome coronavirus 2 omicron variant.

Author

Lam, Lok Ka, Tan, Jing Tong, Ooi, Poh Hwa, Zhang, Ruiqi, Chan, Kwok Hung, Mao, Xianhua, Hung, Ivan F N, Seto, Wai Kay, Yuen, Man Fung, and Cheung, Ka Shing

Subjects
SARS-CoV-2, SARS-CoV-2 Omicron variant, DISEASE risk factors, FATTY liver, PROTON pump inhibitors
Abstract

Background and Aim: We aimed to investigate the effect of metabolic dysfunction‐associated steatotic liver disease (MASLD) on three‐dose BNT162b2 immunogenicity to the omicron variant. Methods: Adult recipients of three doses of BNT162b2 were prospectively recruited between May and December 2021. The serology of the neutralizing antibody by live virus microneutralization (vMN) to the omicron variant was measured at baseline, day 180, and day 360 after the first dose. The primary outcome was seroconversion (vMN titer ≥ 10) at day 360. Exposure of interest was MASLD, defined as hepatic steatosis (controlled attenuation parameter ≥ 248 dB/m on transient elastography) plus at least one of five cardiometabolic risk factors. Subjects with prior COVID‐19 were excluded. A multivariable logistic regression model was used to derive the adjusted odds ratio of seroconversion with MASLD by adjusting for age, sex, antibiotic use, and proton pump inhibitor use. Results: One hundred forty‐eight BNT162b2 recipients (male: 48 [32.4%]; median age: 51.0 years [interquartile range, IQR: 44.5–57.3]) were recruited. The median time from the first dose to the third dose was 8.5 months (IQR: 7.9–8.9). MASLD subjects had a lower seroconversion rate than non‐MASLD ones (89.6% vs 99.0%; P = 0.007). MASLD was the only independent risk factor for seroconversion (adjusted odds ratio: 0.051, 95% confidence interval: 0.002–0.440). Subgroup analysis of immunogenicity at 4 months after the third dose shows significantly lower vMN titer (13.06 [IQR: 7.69–22.20] vs 33.49 [IQR: 24.05–46.53]; P = 0.004) and seroconversion rate (76.9% vs 97.4%; P = 0.016) in MASLD than non‐MASLD subjects, but not within 4 months from the third dose (vMN titer: 46.87 [IQR: 33.12–66.02] vs 41.86 [IQR: 34.47–50.91], P = 0.240; seroconversion rate: 94.3% vs 100%, P = 0.131). Conclusion: Metabolic dysfunction‐associated steatotic liver disease was a risk factor for poorer immunogenicity to the omicron variant, with a more pronounced waning effect compared among three‐dose BNT162b2 recipients. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Optimal glycaemic control and the reduced risk of colorectal adenoma and cancer in patients with diabetes: a population-based cohort study

Author

Mao, Xianhua, primary, Cheung, Ka Shing, additional, Tan, Jing-Tong, additional, Mak, Lung-Yi, additional, Lee, Chi-Ho, additional, Chiang, Chi-Leung, additional, Cheng, Ho Ming, additional, Hui, Rex Wan-Hin, additional, Yuen, Man Fung, additional, Leung, Wai Keung, additional, and Seto, Wai-Kay, additional

Published
2024
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16. “Fatty” or “steatotic”: Position statement from a linguistic perspective by the Chinese-speaking community

Author

Miao, Lei, Ye, Shu-Mian, Fan, Jian-Gao, Seto, Wai-Kay, Yu, Hon Ho, Yu, Ming-Lung, Kao, Jia-Horng, Boon-Bee Goh, George, Young, Dan Yock, Wong, Yu Jun, Chan, Wah-Kheong, Yang, Wah, Jia, Jidong, Lau, George, Wei, Lai, Shi, Junping, Zhang, Huijie, Bi, Yan, Pik-Shan Kong, Alice, Pan, Calvin Q., Zheng, Ming-Hua, Liang, Huiqing, Yang, Ling, Li, Xinhua, Zeng, Qing-Lei, Gao, Rong, Hu, Songhao, Yan, Bi, Jin, Xiaozhi, Li, Gang, Chen, En-Qiang, Hu, Dandan, Fan, Xiaotang, Hu, Peng, Chang, Xiangrong, Jin, Yihui, Cai, Yijing, Chen, Liangmiao, Wen, Qianjun, Sun, Jian, Xu, Hexiang, Li, Junfeng, Yang, Yongping, Huang, Ang, Zhang, Dongmei, Tan, Lin, Li, Dongdong, Zhu, Yueyong, Cai, Chenxi, Gu, Xuemei, Shen, Jilong, Zhong, Jianhong, Li, Lu, Li, Zhenzhen, Ma, Chiye, Liu, Yaming, Zhang, Yimin, Zhao, Lei, Han, Juqiang, Chen, Tao, Zhang, Qiang, Yang, Song, Zhang, Le, Chen, Lanlan, Feng, Gong, Wang, Qixia, Hao, Kunyan, Lu, Qinghua, Mao, Yimin, Zhong, Yandan, Wang, Ningjian, Xin, Yongning, Yu, Yongtao, Qi, Xingshun, Wang, Ke, He, Yingli, Du, Mulong, Zou, Zhengsheng, Xia, Mingfeng, Zhao, Suxian, Zhao, Jingjie, Xie, Wen, Zhang, Yao, Ji, Mao, Richeng, Du, Qingwei, Chen, Haitao, Song, Yongfeng, Wang, Cunchuan, Lu, Yan, Song, Yu, Zhang, Chi, Shi, Li, Mak, Lungyi, Chen, Li, Xu, Liang, Yuan, Hai-Yang, Hong, Liang, Hai, Li, Wu, Xiaoning, Yang, Naibin, Li, Jing-Wei, Jiejin, Zou, Zhuolin, Zheng, Wen, Zhao, Jian, Zhang, Xiang, Huang, Chen-Xiao, Yao, Ying, Yuan, Bao-Hong, Huang, Shanshan, Min, Lian, Chai, Jin, Hong, Wandong, Miao, Kai-Wen, Xiao, Tie, Chen, Shun-Ping, Ye, Feng, Song, Yuhu, Zhang, Jinshun, Zhou, Xiao-Dong, Wang, Mingwei, Dai, Kai, Lou, Jianjun, Duan, Xu, Yu, Hongyan, Jin, Xi, Fu, Liyun, Zhang, Yanliang, Ye, Junzhao, Liu, Feng, Chen, Qin-Fen, Zhou, Yong-Hai, Duan, Xiaohua, Zhang, Qun, Zhang, Faming, Cao, Zhujun, Li, Yingxu, Sun, Dan-Qin, Hu, Ai-Rong, Liu, Fenghua, Chen, Yuanwen, Zhang, Dianbao, Gao, Feng, Ye, Hua, Rao, Huiying, Luo, Kaizhong, Dai, Zhijuan, Wang, Chia-Chi, Tang, Shanhong, Hua, Jing, Deng, Cunliang, Zhou, Ling, Fan, Yu-Chen, Wu, Mingyue, Lu, Hongyan, Zhang, Xiaoxun, Zhang, Huai, Ni, Yan, Kei Ng, Stephen Ka, Li, Chunming, Liu, Chang, Zhang, Xia, Shi, Yu, Yan, Hongmei, Xu, Jinghang, Zhou, Yu-Jie, Cheng, Yuan, Bai, Honglian, Hu, Xiang, Gao, Yufeng, Lin, Biaoyang, Gu, Guangxiang, Chen, Jin, Hu, Xiaoli, Yuan, Xiwei, Wang, Jie, Chen, Qiang, Yiling, Li, Zhu, Xiao Jia, Chen, Xu, Zhu, Yongfen, Liu, Xiaolin, Wang, Bing, Cai, Mingyan, Chen, Enguang, Chen, Jun, Chen, Jingshe, Deng, Hong, Chen, Xiaoxin, Chen, Yingxiao, Cheng, Xinran, Chen, Fei, Ding, Yang, Dong, Zhixia, Ding, Yanhua, Qingxian, Cai, Deng, Zerun, Cai, Tingchen, Chen, Yaxi, Chen, Zhongwei, Chen, Xing, Huang, Jiaofeng, Huang, Mingxing, Fu, Lei, Jin, Jianhong, Geng, Bin, Chen, Yu, Chen, Ruicong, Jin, Weimin, Li, Dongliang, Jin, Xianghong, Li, Jian-Jun, Zhang, Jie, Matsiyit, Alimjan, Wang, Guiqi, Gao, Tian, Zhang, Shu, Yan, Wenmao, Liu, Jie, Chen, Peng, Hu, Hao, Li, Ming, Yuan, Ping Ge, Chen, Yi, Dong, Zhiyong, Li, Xiaopeng, Lin, Su, Li, Jie, Li Ang, Xujing, Liu, Xin, Liu, Shousheng, Li, Min-Dian, Qian, Hui, Qi, Minghua, Peng, Liang, Luo, Fei, Dang, Shuangsuo, Mao, Xianhua, Sheng, Qiyue, Lyu, Jiaojian, Liu, Chenghai, Qi, Kemin, Ma, Honglei, Lu, Zhonghua, Pan, Qiong, Miao, Qing, Li, Xiaosong, Lin, Huapeng, Shui, Guanghou, Qu, Shen, Fei, Wang, Liu, Chang-Hai, Xia, Fan, Wang, Dan, Pan, Ziyan, Hu, Fangzheng, Xu, Long, Xiong, Qing-Fang, Yang, Rui-Xu, Wang, Qi, Chen, Ligang, W Ang, Danny, Ren, Wanhua, Tong, Xiaofei, You, Ningning, Xing, Yanqing, Sun, Chao, Yu, Zhuo, Shuangxu, Xu, Honghai, Sun, Yi, Zhang, Taotao, Wu, Wei, Zhang, Yingmei, Ye, Qing, Zhang, Zhongheng, Yan, Jie, Zhou, Bengjie, Liu, Weiqiang, Li, Yongguo, Zhao, Lili, Lei, Siyi, Zhu, Guangqi, Ouyang, Huang, Zhou, Yaoyao, Yin, Jianhui, Xia, Yongsheng, He, Qiancheng, Zhang, Xiaoyong, Yang, Qiao, Yao, Libin, Pan, Xiazhen, Wang, Xiaodong, Li, Yangyang, Zhu, Shenghao, Zhao, Xinyan, Chen, Sui-Dan, Zhu, Jiansheng, Zeng, Jing, Tang, Liangjie, Hu, Kunpeng, Yang, Wanshui, Huang, Bingyuan, Zhuang, Chengle, Xun, Yunhao, Zhou, Jianghua, Xu, Wenjing, Wu, Bian, Zhang, Xuewu, He, Yong, Mei, Zubing, Xia, Zefeng, Lu, Bin Feng, Li, Qiang, Li, Jia, Yan, Xuebing, Wen, Zhengrong, Liu, Wenyue, Xu, Dongsheng, Chen, Huiting, Wang, Jing, Song, Juan, Peng, Jie, Chen, Jionghuang, Li, Shuchen, Zheng, Yongping, Zhi-Zhi, Xing, Tang, Jieting, Liu, Chuan, Chen, Chao, Guicheng, Wu, Ye, Quanzhong, Ka, Li, Zhou, Yuping, Jia, Xiaoli, Zou, Ziyuan, Zu, Fuqiang, Cai, Yongqian, Chen, Yunzhi, Chu, Jinguo, Yan, Bing, Wang, Tie, Pan, Qiuwei, Xie, Lingling, Zeng, Xufen, Liu, Bingrong, Su, Minghua, Mu, Yibing, Zeng, Menghua, Guo, Yuntong, Yang, Yongfeng, Zhang, Xiaoguan, Wu, Shike, Pan, Jin-Shui, Cao, Li, Feng, Wenhuan, Yubin, Yang, Wang, Na, Lu, Xiaolan, Lu, Guanhua, Xiong, Jianbo, Zhuang, Jianbin, Shi, Guojun, Zhu, Yanfei, Ying, Xing, Qiao, Zengpei, Zhang, Rui, Li, Yuting, Lei, Yuanli, Xixi, Wu, Tian, Na, Lian, Liyou, Zhang, Binbin, Xiaozhu, Huang, Yan, Chen, Wenying, Liu, Kun, Zhang, Ruinan, Lai, Qintao, Wang, Fudi, Wen, Caiyun, Zhang, Xinlei, Wu, Lili, Liang, Yaqin, Jie, You, Xinzhejin, Zeng, Qiqiang, Zhu, Qiang, Chao, Zheng, Shou, Lan, Jin, Wei-Lin, Ye, Chenhui, Han, Yu, Xie, Gangqiao, Zhao, Jing, Ye, Chunyan, Wang, Hua, Song, Lintao, Feng, Juan, Huang, Yubei, Su, Wen, Bai, Juli, Wong, Vincent, Wang, Huifeng, Ming, Wai-Kit, Yu, Yue-Cheng, Jin, Yan, Zhao, Yan, Gao, Lilian, Liangwang, Chen, Hanbin, Ruifangwang, Tang, Yuhan, Chen, Gang, Liu, Dabin, Cai, Xiaobo, Xue, Feng, Yang, Qinhe, Sun, Guangyong, Zhu, Chunxia, Huang, Zhifeng, Zhou, Hongwen, Xiao, Xiao, Hou, Xin, He, Jie, Ji, Dong, Xiao, Huanming, Chi, Xiaoling, Zou, Huaibin, Shi, Yiwen, Fan, Xingliang, Hu, Xiaoyu, Huang, Zhouqin, Cao, Haixia, Jiang, Jingjing, Zhao, Qiang, Chen, Wei, Li, Shi Bo, Zhang, Fan, Chen, Zhiyun, Liu, Jinfeng, Li, Shibo, Liu, Jing, Li, Li, Li, Ruyu, Kun, Ya, Xiao, ErHui, Wang, Tingyao, Wang, Chunjiong, Aili, Aikebaier, Liu, Xiaoxia, Ding, Ran, Zhu, Chonggui, Zeng, Xin, Wu, Miao, Li, Zhen, Yang, Tao, Qin, Yunfei, Sun, Lihua, Xu, Ying, Fu, Xianghui, Li, Yongyin, and Ye, Shumian

Published
2025
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17. Changing prevalence of chronic hepatitis B virus infection in China between 1973 and 2021: a systematic literature review and meta-analysis of 3740 studies and 231 million people

Author

Liu, Zhenqiu, primary, Lin, Chunqing, additional, Mao, Xianhua, additional, Guo, Chengnan, additional, Suo, Chen, additional, Zhu, Dongliang, additional, Jiang, Wei, additional, Li, Yi, additional, Fan, Jiahui, additional, Song, Ci, additional, Zhang, Tiejun, additional, Jin, Li, additional, De Martel, Catherine, additional, Clifford, Gary M, additional, and Chen, Xingdong, additional

Published
2023
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18. Glycemic control as a modifiable and independent risk factor for the development of liver, biliary tract and pancreatic cancer: a territory-wide study of 273,421 patients with diabetes mellitus

Author

Mao, Xianhua, primary, Cheung, Ka Shing, additional, Tan, Jing Tong, additional, Mak, Lung-Yi, additional, Lee, Chi Ho, additional, Chiang, CL, additional, Cheng, Ho Ming, additional, Hui, Rex Wan-Hin, additional, Yuen, Man-Fung, additional, Leung, Wai Keung, additional, and Seto, Wai-Kay, additional

Published
2023
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20. Steatosis, HBV‐related HCC, cirrhosis, and HBsAg seroclearance: A systematic review and meta‐analysis

Author

Mao, Xianhua, primary, Cheung, Ka Shing, additional, Peng, Chengzhi, additional, Mak, Lung‐Yi, additional, Cheng, Ho Ming, additional, Fung, James, additional, Peleg, Noam, additional, Leung, Howard H.‐W., additional, Kumar, Rajneesh, additional, Lee, Jeong‐Hoon, additional, Shlomai, Amir, additional, Yuen, Man‐Fung, additional, and Seto, Wai‐Kay, additional

Published
2023
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22. Glycemic Control and Variability are Risk Factors for Hepatocellular Carcinoma and Liver-Related Mortality in a General Population with Diabetes Mellitus

Author

Mao, Xianhua, primary, Cheung, Kar-Shing, additional, Tan, Jing-Tong, additional, Mak, Lung-Yi Loey, additional, Lee, Chi-Ho, additional, Chiang, Chi-Leung, additional, Cheng, Ho Ming, additional, Hui, Rex Wan-Hin, additional, Leung, Wai K., additional, Yuen, Man-Fung, additional, and Seto, Wai-Kay, additional

Published
2023
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23. Hepatitis B virus pre-genomic RNA and hepatitis B core-related antigen reductions at week 4 predict favourable hepatitis B surface antigen response upon long-term nucleos(t)ide analogue in chronic hepatitis B

Author

Mak, Lung-Yi, primary, Wong, Danny, additional, Kuchta, Alison, additional, Hilfiker, Martina, additional, Hamilton, Aaron, additional, Chow, Ning, additional, Mao, XianHua, additional, Seto, Wai Kay, additional, and Yuen, Man-Fung, additional

Published
2023
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24. Synergistic association of sodium-glucose cotransporter-2 inhibitor and metformin on liver and non-liver complications in patients with type 2 diabetes mellitus and metabolic dysfunction-associated steatotic liver disease

Author

Mao, Xianhua, Zhang, Xinrong, Kam, Leslie, Chien, Nicholas, Lai, Rongtao, Cheung, Ka-Shing, Yuen, Man-Fung, Cheung, Ramsey, Seto, Wai-Kay, and Nguyen, Mindie H

Abstract

ObjectiveType 2 diabetes mellitus and metabolic dysfunction-associated steatotic liver disease (diabetic MASLD) frequently coexist and worsen liver and non-liver outcomes, but effective pharmacological therapies are limited. We aimed to evaluate the long-term effect of sodium-glucose cotransporter-2 inhibitor (SGLT-2i) on liver and non-liver outcomes among patients with diabetic MASLD.DesignThis population-based cohort study retrieved patients with diabetic MASLD from Merative Marketscan Research Databases (April 2013 and December 2021). The active comparator was other glucose-lowering drugs (oGLDs). Primary outcomes were liver complications including hepatocellular carcinoma (HCC) and liver cirrhosis, as well as non-liver complications including cardiovascular disease (CVD), chronic kidney disease (CKD) and non-liver cancer. Propensity score matching was applied and Cox regression models were conducted.ResultsCompared with oGLD, SGLT-2i users had significantly lower risk of HCC (HR 0.76, 95% CI 0.62 to 0.93), liver cirrhosis (HR 0.80, 95% CI 0.76 to 0.84), CVD (HR 0.82, 95% CI 0.79 to 0.85) and CKD (HR 0.66, 95% CI 0.62 to 0.70), non-liver cancer (HR 0.81, 95% CI 0.76 to 0.86). Compared with patients without metformin and SGLT-2i, a stepwise decreasing risk was observed in users of either metformin or SGLT-2i (HRs 0.76–0.97) and in users of both medications (HRs 0.58–0.79). The lower risk also was shown in liver decompensation, compensated cirrhosis, major CVD, end-stage renal disease and specific common cancers (HRs 0.61–0.84).ConclusionIn a nationwide cohort, SGLT-2i users were associated with a substantially lower risk of liver and non-liver complications than oGLD users among patients with diabetic MASLD. The risk was further reduced with concomitant metformin use.

Published
2024
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33. Chinese Translation and Psychometric Testing of the Simplified Version of Social Support Scale for Physical Activity

Author

Mao,Xia, Mao,Xianhua, Yu,Yu, Guo,Jia, Mao,Xia, Mao,Xianhua, Yu,Yu, and Guo,Jia

Abstract

Xia Mao,1 Xianhua Mao,1 Yu Yu,2 Jia Guo3 1Department of Nursing, Yueyang Vocational Technical College, Yueyang, People’s Republic of China; 2Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; 3Department of Clinical Nursing, Xiangya School of Nursing, Central South University, Changsha, People’s Republic of ChinaCorrespondence: Jia Guo, Department of Clinical Nursing, Xiangya School of Nursing, Central South University, Changsha, People’s Republic of China, Tel +86 13875947418, Fax +86 0731-82650266, Email guojia621@163.comPurpose: Sallis et al’s social support scale for physical activity (SSSPH) is one of the most widely used scales to measure exercise-related social support yet has never been translated and validated in Chinese. The current study aims to simplify, translate, and validate a short form of SSSPH in a Chinese population.Patients and Methods: A simplified Chinese version of the SSSPH (SSSPH-SC) retaining 6 items was achieved after the translation and back-translation process, which showed good content validity. The scale was then assessed among a convenience sample of 266 Chinese adults from various backgrounds. Internal consistency was tested by calculating Cronbach’s α. The a priori two-factor structure was tested with confirmatory factor analysis (CFA). Concurrent validity was examined by investigating the correlation of the SSSPH-SC with general social support, quality of life, and self-rated health.Results: The SSSPH-SC full scale and subscales showed good internal consistency with Cronbach’s alpha ranging from 0.87 to 0.92. The CFA supported the a priori two-factor structure: family support and friend support, with χ 2/df=2.93, CFI = 0.98, TLI = 0.98, RMSEA = 0.07, SRMR = 0.035. The concurrent validity of the SSSPH-CS was further supported by its significant positive correlations with social support (r=0.26, p< 0.001), quality of life (r=0.25, p< 0.001), and self-rated general health depression (r=0.23, p&l

Published
2022

43. Early prediction of mortality risk among patients with severe COVID-19, using machine learning

Author

Hu, Chuanyu, primary, Liu, Zhenqiu, additional, Jiang, Yanfeng, additional, Shi, Oumin, additional, Zhang, Xin, additional, Xu, Kelin, additional, Suo, Chen, additional, Wang, Qin, additional, Song, Yujing, additional, Yu, Kangkang, additional, Mao, Xianhua, additional, Wu, Xuefu, additional, Wu, Mingshan, additional, Shi, Tingting, additional, Jiang, Wei, additional, Mu, Lina, additional, Tully, Damien C, additional, Xu, Lei, additional, Jin, Li, additional, Li, Shusheng, additional, Tao, Xuejin, additional, Zhang, Tiejun, additional, and Chen, Xingdong, additional

Published
2020
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44. Cover Image

Author

Fan, Jiahui, primary, Liu, Zhenqiu, additional, Mao, Xianhua, additional, Tong, Xin, additional, Zhang, Tiejun, additional, Suo, Chen, additional, and Chen, Xingdong, additional

Published
2020
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46. Early prediction of mortality risk among severe COVID-19 patients using machine learning

Author

Hu, Chuanyu, primary, Liu, Zhenqiu, additional, Jiang, Yanfeng, additional, Zhang, Xin, additional, Shi, Oumin, additional, Xu, Kelin, additional, Suo, Chen, additional, Wang, Qin, additional, Song, Yujing, additional, Yu, Kangkang, additional, Mao, Xianhua, additional, Wu, Xuefu, additional, Wu, Mingshan, additional, Shi, Tingting, additional, Jiang, Wei, additional, Mu, Lina, additional, Tully, Damien C, additional, Xu, Lei, additional, Jin, Li, additional, Li, Shusheng, additional, Tao, Xuejin, additional, Zhang, Tiejun, additional, and Chen, Xingdong, additional

Published
2020
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49. Production and characterization of a novel beverage from maize silk through fermentation with kombucha consortium

Author

Liu Kai, Mao Xianhua, Wang Zhiwen, Sun Yijiao, and Yuan Fahu

Subjects
Kombucha, SILK, Fermentation, Food science, Biology
Abstract

Maize silk was taken as raw material, boiled, cooled and fermented by kombucha fungus to prepare maize silk kombucha beverage. The inoculation amount, initial sugar content and fermentation temperature of kombucha were selected as the influencing factors, and the total acid content of kombucha beverage was taken as the response value. Based on the single factor test, the Box-Benhnken response surface methodology was used to optimize the fermentation process. The results showed that the optimal fermentation conditions were as follows: kombucha fungus inoculation amount of 17%, initial sugar content of 18%, fermentation temperature of 30 °C, the total acid content of Kombucha beverage was 4.85 g/dL, which was basically consistent with the predicted value of the model.

Published
2021
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