University of Birmingham

About: University of Birmingham is a education organization based out in Birmingham, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 51794 authors who have published 115304 publications receiving 4335316 citations. The organization is also known as: Birmingham University & Uni of Birmingham.

Broadly neutralizing antibodies protect against hepatitis C virus quasispecies challenge

Abstract: A major problem in hepatitis C virus (HCV) immunotherapy or vaccine design is the extreme variability of the virus. We identified human monoclonal antibodies (mAbs) that neutralize genetically diverse HCV isolates and protect against heterologous HCV quasispecies challenge in a human liver-chimeric mouse model. The results provide evidence that broadly neutralizing antibodies to HCV protect against heterologous viral infection and suggest that a prophylactic vaccine against HCV may be achievable.

Enhanced detection of circulating tumor DNA by fragment size analysis

Abstract: Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for noninvasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 patients with cancer using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90 and 150 bp and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90 and 150 bp improved detection of tumor DNA, with more than twofold median enrichment in >95% of cases and more than fourfold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with area under the curve (AUC) >0.99 compared to AUC 0.91 compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA.

combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Abstract: multiple imputation (mi) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. the multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. the estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. the resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. however, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. a literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. methods for combining all reported estimates after mi were not well reported in the current literature. rubin's rules without applying any transformations were the standard approach used, when any method was stated. the proposed simple guidelines for combining estimates after mi may lead to a wider and more appropriate use of mi in future prognostic modelling studies.