Search PubMedSearch

Biomedical subjects

Yinshan Zhao

Publications and source records attributed to Yinshan Zhao.

2 recordsLinked to original sources

Disease-modifying therapy uptake in a pediatric-onset multiple sclerosis population, British Columbia, Canada.

BACKGROUND: The treatment of pediatric-onset multiple sclerosis (POMS) is evolving as disease-modifying therapies (DMTs) undergo pediatric clinical trials. However, most DMTs remain off-label in children resulting in barriers to access and variability in treatment patterns across jurisdictions. The aim of this study was to describe trends in DMT uptake in POMS in British Columbia, Canada. METHODS: We utilized linked clinical and administrative datasets to identify MS with onset <18&#xa0;years-of-age and assess DMT uptake from dispensed prescriptions between January 1, 1996 and March 1, 2020 in British Columbia, Canada. RESULTS: Of 173 POMS cases (median follow-up&#xa0;=&#xa0;11.5&#xa0;years), 32 (18%) filled a DMT prescription <18&#xa0;years-of-age; 76 (44%) did so at any age. Moderate-efficacy therapies (beta-interferons/glatiramer acetate/teriflunomide/dimethyl fumarate) were the initial DMT in 60 cases (79%) overall, however by the end of follow-up a high-efficacy therapy was the most recent DMT dispensed for 52/76 cases (68%). After 2016, anti-CD20 monoclonal antibodies became the most common DMT class dispensed. CONCLUSIONS: Moderate-efficacy therapies were the more common initial DMT dispensed for individuals with POMS, but more than two-thirds eventually received a high-efficacy therapy. Only a minority of POMS cases were first dispensed a DMT under age 18&#xa0;years.

Disease modifying therapies

Proportionality-based association metrics in count compositional data.

Compositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA sequencing are compositional in nature. In this kind of data, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, an inherent bias arises when these metrics are calculated empirically on count-based measures due to variability in read depths. We quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data and are applied to a mouse embryonic stem cell single-cell sequencing dataset, as well as a pediatric-onset multiple sclerosis metagenomic dataset.

Animals