Automated Author ProfileDhelia M. Williamson
Dhelia M. Williamson
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: 2.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
ABSTRACTExposure to heavy metals and organic solvents are potential etiologic factors for multiple sclerosis (MS), but their interaction with MS-associated genes is under-studied. The authors explored the relationship between environmental exposure to lead, mercury, and solvents and 58 single-nucleotide polymorphisms (SNPs) in MS-associated genes. Data from a population-based case-control study of 217 prevalent MS cases and 496 age-, race-, gender-, and geographically matched controls were used to fit conditional logistic regression models of the association between the chemical, gene, and MS, adjusting for education and ancestry. MS cases were more likely than controls to report lead (odds ratio [OR] = 2.03; 95% confidence interval [CI]: 1.07, 3.86) and mercury exposure (OR = 2.06; 95% CI: 1.08, 3.91). Findings of potential gene-environment interactions between SNPs in TNF-α, TNF-β, TCA-β, VDR, MBP, and APOE, and lead, mercury, or solvents should be considered cautiously due to limited sample size.
Authors
- Poole, Charles ;
- Satten, Glen A. ;
- Ashley-Koch, Allison ;
- Napier, Melanie D. ;
- Marrie, Ruth Ann ;
- Dhelia M. Williamson
ABSTRACTExposure to heavy metals and organic solvents are potential etiologic factors for multiple sclerosis (MS), but their interaction with MS-associated genes is under-studied. The authors explored the relationship between environmental exposure to lead, mercury, and solvents and 58 single-nucleotide polymorphisms (SNPs) in MS-associated genes. Data from a population-based case-control study of 217 prevalent MS cases and 496 age-, race-, gender-, and geographically matched controls were used to fit conditional logistic regression models of the association between the chemical, gene, and MS, adjusting for education and ancestry. MS cases were more likely than controls to report lead (odds ratio [OR] = 2.03; 95% confidence interval [CI]: 1.07, 3.86) and mercury exposure (OR = 2.06; 95% CI: 1.08, 3.91). Findings of potential gene-environment interactions between SNPs in TNF-α, TNF-β, TCA-β, VDR, MBP, and APOE, and lead, mercury, or solvents should be considered cautiously due to limited sample size.
Authors
- Poole, Charles ;
- Satten, Glen A. ;
- Ashley-Koch, Allison ;
- Napier, Melanie D. ;
- Marrie, Ruth Ann ;
- Dhelia M. Williamson