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Your search keyword '"Zhang, Furen"' showing total 8 results
Start Over Author "Zhang, Furen" Journal plos neglected tropical diseases
8 results on '"Zhang, Furen"'

1. Single-cell sequencing analysis reveals development and differentiation trajectory of Schwann cells manipulated by M. leprae

Author

Ma, Shanshan, primary, Mi, Zihao, additional, Wang, Zhenzhen, additional, Sun, Lele, additional, Liu, Tingting, additional, Shi, Peidian, additional, Wang, Chuan, additional, Xue, Xiaotong, additional, Chen, Wenjie, additional, Wang, Zhe, additional, Yu, Yueqian, additional, Zhang, Yuan, additional, Bao, Fangfang, additional, Wang, Na, additional, Wang, Honglei, additional, Xia, Qianqian, additional, Liu, Hong, additional, Sun, Yonghu, additional, and Zhang, Furen, additional

Published
2023
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2. Validation study of HLA-B*13:01 as a biomarker of dapsone hypersensitivity syndrome in leprosy patients in Indonesia

Author

Krismawati, Hana, primary, Irwanto, Astrid, additional, Pongtiku, Arry, additional, Irwan, Ishak Darryl, additional, Maladan, Yustinus, additional, Sitanggang, Yuli Arisanti, additional, Wahyuni, Tri, additional, Tanjung, Ratna, additional, Sun, Yonghu, additional, Liu, Hong, additional, Zhang, Furen, additional, Oktavian, Antonius, additional, and Liu, Jianjun, additional

Published
2020
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3. The role of an active surveillance strategy of targeting household and neighborhood contacts related to leprosy cases released from treatment in a low-endemic area of China

Author

Wang, Na, primary, Chu, Tongsheng, additional, Li, Furong, additional, Wang, Zhenzhen, additional, Liu, Dianchang, additional, Chen, Mingfei, additional, Wang, Honglei, additional, Niu, Guiye, additional, Liu, Dan, additional, Zhang, Mingkai, additional, Xu, Yuanyuan, additional, Zhang, Yan, additional, Li, Jinghui, additional, Li, Zhen, additional, You, Jiabao, additional, Mao, Liguo, additional, Li, Huaizhang, additional, Chen, Yongjin, additional, Liu, Hong, additional, and Zhang, Furen, additional

Published
2020
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6. Prediction of leprosy in the Chinese population based on a weighted genetic risk score

Author

Wang, Na, primary, Wang, Zhenzhen, additional, Wang, Chuan, additional, Fu, Xi'an, additional, Yu, Gongqi, additional, Yue, Zhenhua, additional, Liu, Tingting, additional, Zhang, Huimin, additional, Li, Lulu, additional, Chen, Mingfei, additional, Wang, Honglei, additional, Niu, Guiye, additional, Liu, Dan, additional, Zhang, Mingkai, additional, Xu, Yuanyuan, additional, Zhang, Yan, additional, Li, Jinghui, additional, Li, Zhen, additional, You, Jiabao, additional, Chu, Tongsheng, additional, Li, Furong, additional, Liu, Dianchang, additional, Liu, Hong, additional, and Zhang, Furen, additional

Published
2018
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7. Prediction of leprosy in the Chinese population based on a weighted genetic risk score.

Author

Fu, Xi'an, Wang, Na, Yue, Zhenhua, Wang, Honglei, Niu, Guiye, Li, Jinghui, Li, Zhen, You, Jiabao, Liu, Hong, Zhang, Furen, Chen, Mingfei, Li, Furong, Wang, Zhenzhen, Wang, Chuan, Yu, Gongqi, Liu, Tingting, Zhang, Huimin, Li, Lulu, Liu, Dan, and Zhang, Mingkai

Subjects
HANSEN'S disease, GENETICS -- Risk factors, PREDICTION models, ACCURACY, POPULATION, DISEASE risk factors
Abstract

Genome wide association studies (GWASs) have revealed multiple genetic variants associated with leprosy in the Chinese population. The aim of our study was to utilize the genetic variants to construct a risk prediction model through a weighted genetic risk score (GRS) in a Chinese set and to further assess the performance of the model in identifying higher-risk contact individuals in an independent set. The highest prediction accuracy, with an area under the curve (AUC) of 0.743 (95% confidence interval (CI): 0.729–0.757), was achieved with a GRS encompassing 25 GWAS variants in a discovery set that included 2,144 people affected by leprosy and 2,671 controls. Individuals in the high-risk group, based on genetic factors (GRS > 28.06), have a 24.65 higher odds ratio (OR) for developing leprosy relative to those in the low-risk group (GRS≤18.17). The model was then applied to a validation set consisting of 1,385 people affected by leprosy and 7,541 individuals in contact with leprosy, which yielded a discriminatory ability with an AUC of 0.707 (95% CI: 0.691–0.723). When a GRS cut-off value of 22.38 was selected with the optimal sensitivity and specificity, it was found that 39.31% of high risk contact individuals should be screened in order to detect leprosy in 64.9% of those people affected by leprosy. In summary, we developed and validated a risk model for the prediction of leprosy that showed good discrimination capabilities, which may help physicians in the identification of patients coming into contact with leprosy and are at a higher-risk of developing this condition. [ABSTRACT FROM AUTHOR]

Published
2018
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