Automated Author ProfileFan, Hui-Hui
Wenzhou Medical University0000-0003-0918-9797
Fan, Hui-Hui
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Background: Environmental substances such as pesticides are well-known in link with PD risk. Enzymes including cytochromes P450 (CYPs), esterases and glutathione S-transferases (GSTs) are responsible for the xenobiotic metabolism and may functionally compensate each other for subtypes in the same class. We hypothesize that the genetic effects of each class modulate PD risk stronger in a synergistic way than individually. Methods: We selected 14 polymorphic loci out of 13 enzymes in the classes of CYP, esterase, and GST, and recruited a cohort of 1026 PD and control subjects from eastern China. The genotypes were identified using improved multiplex ligation detection reaction, and analyzed using multiple models. Results: A total of 13 polymorphisms remained after Hardy-Weinberg equilibrium analysis. None of the polymorphisms were independently associated with PD risk after Bonferroni correction either by logistic regression or genetic models. In contrast, interaction analyses detected increased resistance to PD risk in individuals carrying the rs12441817/CC (CYP1A1) and rs2070676/G (CYP2E1) genotypes (P = 0.002, OR = 0.393, 95% CI = 0.216-0.715), or carrying the GSTM1-present, GSTT1-null, rs156697/G (GSTO2) and rs1695/AA (GSTP1) genotypes (P = 0.003, OR = 0.348, 95% CI = 0.171-0.706), but not with genotypes of esterases. Conclusions: We demonstrate a presence of synergistic but not individual impact on PD susceptibility in polymorphisms of CYPs and GSTs. The results indicate that the genetic interplay leads the way to PD development for xenobiotic metabolizing enzymes.
Authors
- Fan, Hui-Hui ;
- Li, Bao-Qing ;
- Wu, Ke-Yun ;
- Yan, Hai-Dan ;
- Gu, Meng-Jie ;
- Yao, Xing-Hao ;
- Dong, Hao-Jia ;
- Zhang, Xiong ;
- Zhu, Jian-Hong