Feasibility and cost-benefit of implementing pooled screening for HCVAg in small blood bank settings

Novel Strategy for Screening Target Proteins by the Common Drugs─Sofosbuvir-Specific Profiling of HCV Patient Serum

It is the scientific basis of precision medicine to study all of the targets of drugs based on the interaction between drugs and proteins. It is worth paying attention to unknown proteins that interact with drugs to find new targets for the design of new drugs. Herein, we developed a protein profiling strategy based on drug-protein interactions and drug-modified magnetic nanoparticles and took hepatitis C virus (HCV) and its corresponding drug sofosbuvir (SOF) as an example. A SOF-modified magnetic separation medium (Fe3O4@POSS@SOF) was prepared, and a gradient elution strategy was employed and optimized to profile specific proteins interacted with SOF. A series of proteomic analyses were performed to profile proteins based on SOF-protein interactions (SPIs) in the serum of HCV patients to evaluate the specificity of the profiling strategy. As a result, five proteins were profiled with strong SPIs and exhibited high relevance with liver tissue, which were potentially new drug targets. Among them, HSP60 was used to confirm the highly specific interactions between the SOF and its binding proteins by Western blotting analysis. Besides, 124 and 29 differential proteins were profiled by SOF material from three HCV patient serum and pooled 20 HCV patient serum, respectively, by comparing with healthy human serum. In comparison with those profiled by the polyhedral oligomeric silsesquioxane (POSS) material, differential proteins profiled by the SOF material were highly associated with liver diseases through GO analysis and pathway analysis. Furthermore, four common differential proteins profiled by SOF material but not by POSS material were found to be identical and expressed consistently in both pooled serum samples and independent serum samples, which might potentially be biomarkers of HCV infection. Taken together, our study proposes a highly specific protein profiling strategy to display distinctive proteomic profiles, providing a novel idea for drug design and development.

From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data

Due to reductions in both time and cost, group testing is a popular alternative to individual-level testing for disease screening. These reductions are obtained by testing pooled biospecimens (eg, blood, urine, swabs, etc.) for the presence of an infectious agent. However, these reductions come at the expense of data complexity, making the task of conducting disease surveillance more tenuous when compared to using individual-level data. This is because an individual's disease status may be obscured by a group testing protocol and the effect of imperfect testing. Furthermore, unlike individual-level testing, a given participant could be involved in multiple testing outcomes and/or may never be tested individually. To circumvent these complexities and to incorporate all available information, we propose a Bayesian generalized linear mixed model that accommodates data arising from any group testing protocol, estimates unknown assay accuracy probabilities and accounts for potential heterogeneity in the covariate effects across population subgroups (eg, clinic sites, etc.); this latter feature is of key interest to practitioners tasked with conducting disease surveillance. To achieve model selection, our proposal uses spike and slab priors for both fixed and random effects. The methodology is illustrated through numerical studies and is applied to chlamydia surveillance data collected in Iowa.

Bayesian regression for group testing data

Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of a disease. When individual covariate information is available (e.g., age, gender, number of sexual partners, etc.), a common goal is to relate an individual's true disease status to the covariates in a regression model. Estimating this relationship is a nonstandard problem in group testing because true individual statuses are not observed and all testing responses (on pools and on individuals) are subject to misclassification arising from assay error. Previous regression methods for group testing data can be inefficient because they are restricted to using only initial pool responses and/or they make potentially unrealistic assumptions regarding the assay accuracy probabilities. To overcome these limitations, we propose a general Bayesian regression framework for modeling group testing data. The novelty of our approach is that it can be easily implemented with data from any group testing protocol. Furthermore, our approach will simultaneously estimate assay accuracy probabilities (along with the covariate effects) and can even be applied in screening situations where multiple assays are used. We apply our methods to group testing data collected in Iowa as part of statewide screening efforts for chlamydia, and we make user-friendly R code available to practitioners.

Group testing regression models with dilution submodels

Group testing, where specimens are tested initially in pools, is widely used to screen individuals for sexually transmitted diseases. However, a common problem encountered in practice is that group testing can increase the number of false negative test results. This occurs primarily when positive individual specimens within a pool are diluted by negative ones, resulting in positive pools testing negatively. If the goal is to estimate a population-level regression model relating individual disease status to observed covariates, severe bias can result if an adjustment for dilution is not made. Recognizing this as a critical issue, recent binary regression approaches in group testing have utilized continuous biomarker information to acknowledge the effect of dilution. In this paper, we have the same overall goal but take a different approach. We augment existing group testing regression models (that assume no dilution) with a parametric dilution submodel for pool-level sensitivity and estimate all parameters using maximum likelihood. An advantage of our approach is that it does not rely on external biomarker test data, which may not be available in surveillance studies. Furthermore, unlike previous approaches, our framework allows one to formally test whether dilution is present based on the observed group testing data. We use simulation to illustrate the performance of our estimation and inference methods, and we apply these methods to 2 infectious disease data sets.

Utilization management in the blood transfusion service

The scope of activity of the Blood Transfusion Service (BTS) makes it unique among the clinical laboratories. The combination of therapeutic and diagnostic roles necessitates a multi-faceted approach to utilization management in the BTS. We present our experience in utilization management in large academic medical center.