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Biology subjects

Ning, X.

Publications and source records attributed to Ning, X..

2 recordsLinked to original sources

Intra-familial phenotypic heterogeneity and telomere abnormality in von Hippel-Lindau disease

Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome with poor survival. The current recommendations have proposed uniform surveillance strategies for all patients, neglecting the obvious phenotypic varieties. In this study, we aim to confirm the phenotypic heterogeneity in VHL disease and the underlying mechanism. A total of 151 parent-child pairs were enrolled for genetic anticipation analysis, and 77 sibling pairs for birth order effect analysis. Four statistical methods were used to compare the onset age of patients among different generations and different birth orders. The results showed that the average onset age was 18.9 years earlier in children than in their parents, which was statistically significant in all of the four statistical methods. Furthermore, the first-born siblings were affected 8.3 years later than the other ones among the maternal patients. Telomere shortening was confirmed to be associated with genetic anticipation in VHL families, while it failed to explain the birth order effect. Moreover, no significant difference was observed for overall survival between parents and children (p=0.834) and between first-born patients and the other siblings (p=0.390). This study provides definitive evidence and possible mechanisms of intra-familial phenotypic heterogeneity in VHL families, which is helpful to the update of surveillance guidelines.

genetics

Drug Recommendation toward Safe Polypharmacy

Adverse drug reactions (ADRs) induced from high-order drug-drug interactions (DDIs) due to polypharmacy - simultaneous use of multiple drugs - represent a significant public health problem. Unfortunately, computational effects to facilitate future polypharmacy, particularly to assist safe multidrug prescription, are still in their infancy. We formally formulate the to-avoid and safe drug recommendation problems when multiple drugs have been taken simultaneously. We develop a joint model with a recommendation component and an ADR label prediction component to recommend for a prescription a set of to-avoid/safe drugs that will induce/will not induce ADRs if taken together with the prescription. We also develop real drug-drug interaction datasets and corresponding evaluation protocols.

bioinformatics