Automated Author ProfileWei, Z.
Wei, Z.
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: 8.7 (sum of 7 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Objective: To compare the efficacy of autologous serum (AS) eye drops and artificial tears (AT) in dry eye disease (DED). Methods: Five databases (PubMed, Science Direct, the Cochrane Library, the Chinese National Knowledge Infrastructure, and the Wanfang Database) were searched for randomized controlled trials (RCTs). Efficacy was evaluated in terms of the Ocular Surface Disease Index (OSDI), Schirmer I test, tear break-up time (TBUT), and fluorescein and rose bengal staining of ocular surface. The estimated effects of AS or AT were expressed as a proportion with the 95% confidence interval and plotted on a forest plot. Results: Seven RCTs with 267 subjects were included in the meta-analysis. For most of the studies, subjects’ age was around 50 years old, and the mostly treatment duration was within 8 weeks. The follow-up results showed that the OSDI after AS treatment was lower than that after the AT treatment: the mean difference (MD) was –10.75 (95% CI, –18.12; –3.39) points. There was no difference on the Schirmer I test after treatment between the two groups: the MD was 1.68 (95% CI, –0.65; 4.00) mm. The TBUT of the AS group was longer than that of the AT group, with an MD of 4.53 (95% CI, 2.02; 7.05) s. There was no statistically significant difference on fluorescein staining score of the ocular surface between the AS group and the AT group, the MD was –2.53 (95% CI, –6.08; 1.03) points. The rose bengal staining score of the AS group was slightly lower than that of the AT group after treatment: the MD was –0.78 (95% CI, –1.34; –0.22) points. Conclusion: AS could be an effective treatment for DED, improving OSDI, TBUT, and rose bengal staining score. Further RCTs with large samples and long-term follow-up are still needed to determine the exact role of AS in the management of DED.
Authors
- Wang, L. ;
- Cao, K. ;
- Wei, Z. ;
- Baudouin, C. ;
- Labbé, A. ;
- Liang, Q.
Objective: To compare the efficacy of autologous serum (AS) eye drops and artificial tears (AT) in dry eye disease (DED). Methods: Five databases (PubMed, Science Direct, the Cochrane Library, the Chinese National Knowledge Infrastructure, and the Wanfang Database) were searched for randomized controlled trials (RCTs). Efficacy was evaluated in terms of the Ocular Surface Disease Index (OSDI), Schirmer I test, tear break-up time (TBUT), and fluorescein and rose bengal staining of ocular surface. The estimated effects of AS or AT were expressed as a proportion with the 95% confidence interval and plotted on a forest plot. Results: Seven RCTs with 267 subjects were included in the meta-analysis. For most of the studies, subjects’ age was around 50 years old, and the mostly treatment duration was within 8 weeks. The follow-up results showed that the OSDI after AS treatment was lower than that after the AT treatment: the mean difference (MD) was –10.75 (95% CI, –18.12; –3.39) points. There was no difference on the Schirmer I test after treatment between the two groups: the MD was 1.68 (95% CI, –0.65; 4.00) mm. The TBUT of the AS group was longer than that of the AT group, with an MD of 4.53 (95% CI, 2.02; 7.05) s. There was no statistically significant difference on fluorescein staining score of the ocular surface between the AS group and the AT group, the MD was –2.53 (95% CI, –6.08; 1.03) points. The rose bengal staining score of the AS group was slightly lower than that of the AT group after treatment: the MD was –0.78 (95% CI, –1.34; –0.22) points. Conclusion: AS could be an effective treatment for DED, improving OSDI, TBUT, and rose bengal staining score. Further RCTs with large samples and long-term follow-up are still needed to determine the exact role of AS in the management of DED.
Authors
- Wang, L. ;
- Cao, K. ;
- Wei, Z. ;
- Baudouin, C. ;
- Labbé, A. ;
- Liang, Q.
No description available
Authors
- Wei, Z. ;
- Feng, Y.
No description available
Authors
- Wei, Z. ;
- Feng, Y.
Aims: The purpose of this study was to evaluate the effect of angiotensin-converting enzyme inhibitors (ACEIs) on contrast-induced nephropathy (CIN) in patients undergoing coronary angiography or percutaneous coronary intervention (PCI). Methods: We searched the Medline, Embase, Cochrane Library, China National Knowledge Infrastructure, Chongqing VIP database and Wanfang database up to December 2014. Pooled risk ratios (RRs) or weighted mean difference (WMD) with their 95% CIs for the CIN incidence, serum creatinine (SCr), estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN) of the patients were collected and calculated using the software Review Manager 5.2. Results: A total of 12 separate studies including 1,868 patients (1,011 ACEI cases and 857 controls) were considered in the meta-analysis. The overall RR of the incident CIN in the ACEI group vs. the control group was 0.95 (95% CI 0.57-1.58), and the total WMDs of the ΔSCr, ΔeGFR and ΔBUN were -0.01 (95% CI -0.04 to 0.02), 5.71 (95% CI -0.66 to 12.09) and 0.78 (95% CI -0.16 to 1.73), respectively. Besides, the RR of CIN incidence in the captopril group vs. the control group was 0.72 (95% CI 0.25-2.05, p = 0.54), and the pooled WMD of the ΔSCr was -0.13 (95% CI -0.21 to -0.06, p < 0.01). Conclusion: This meta-analysis suggests that ACEIs administration has no significant influence in the CIN of patients undergoing coronary angiography or PCI; however, captopril might have the potential to prevent CIN.
Authors
- Zhou, S. ;
- Wu, C. ;
- Song, Q. ;
- Yang, X. ;
- Wei, Z.
Aims: The purpose of this study was to evaluate the effect of angiotensin-converting enzyme inhibitors (ACEIs) on contrast-induced nephropathy (CIN) in patients undergoing coronary angiography or percutaneous coronary intervention (PCI). Methods: We searched the Medline, Embase, Cochrane Library, China National Knowledge Infrastructure, Chongqing VIP database and Wanfang database up to December 2014. Pooled risk ratios (RRs) or weighted mean difference (WMD) with their 95% CIs for the CIN incidence, serum creatinine (SCr), estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN) of the patients were collected and calculated using the software Review Manager 5.2. Results: A total of 12 separate studies including 1,868 patients (1,011 ACEI cases and 857 controls) were considered in the meta-analysis. The overall RR of the incident CIN in the ACEI group vs. the control group was 0.95 (95% CI 0.57-1.58), and the total WMDs of the ΔSCr, ΔeGFR and ΔBUN were -0.01 (95% CI -0.04 to 0.02), 5.71 (95% CI -0.66 to 12.09) and 0.78 (95% CI -0.16 to 1.73), respectively. Besides, the RR of CIN incidence in the captopril group vs. the control group was 0.72 (95% CI 0.25-2.05, p = 0.54), and the pooled WMD of the ΔSCr was -0.13 (95% CI -0.21 to -0.06, p < 0.01). Conclusion: This meta-analysis suggests that ACEIs administration has no significant influence in the CIN of patients undergoing coronary angiography or PCI; however, captopril might have the potential to prevent CIN.
Authors
- Zhou, S. ;
- Wu, C. ;
- Song, Q. ;
- Yang, X. ;
- Wei, Z.
No description available
Authors
- Xiang, M.S. ;
- Liu, X.W. ;
- Yuan, H.B. ;
- Huang, Y. ;
- Huo, Z.Y. ;
- Zhang, H.W. ;
- Chen, B.Q. ;
- Zhang, H.H. ;
- Sun, N.C. ;
- Wang, C. ;
- Zhao, Y.H. ;
- Shi, J.R. ;
- Luo, A.L. ;
- Li, G.P. ;
- Wu, Y. ;
- Bai, Z.R. ;
- Zhang, Y. ;
- Hou, Y.H. ;
- Yuan, H.L. ;
- Li, G.W. ;
- Wei, Z.