Three-Stage Pooled Plasma Hepatitis C Virus RNA Testing for the Identification of Acute HCV Infections in At-Risk Populations.

Timely diagnosis and treatment of hepatitis C virus (HCV) infection may prevent its transmission. We evaluated the performance and cost reductions of the pooled plasma HCV RNA testing strategy to identify acute HCV infections among people living with HIV (PLWH). PLWH with sexually transmitted infections, elevated aminotransferases within the past 6 months or past HCV infections (high-risk) and those without (low-risk) were enrolled prospectively. Participants underwent three-stage pooled plasma HCV RNA testing every 12 to 24 weeks until detection of HCV RNA or completion of a 48-week follow-up. The three-stage strategy combined 20 individual specimens into a stage 1 pool, 5 individual specimens from the stage 1 pool that tested positive for HCV RNA in the stage 2 mini-pool, followed by testing of individual specimens of the stage 2 mini-pool tested positive for HCV RNA. A simulation was constructed to investigate the cost reductions and pooled sensitivity and specificity under different combinations of HCV prevalence and pool/mini-pool sizes. Between June 25, 2019 and March 31, 2021, 32 cases of incident HCV viremia were identified in 760 high-risk PLWH that were enrolled 834 times, giving an incidence rate of 56.6 per 1000 person-years of follow-up (PYFU). No cases of HCV viremia were identified in 557 low-risk PLWH during a total of 269.2 PYFU. Simulation analysis suggested that this strategy could reduce HCV RNA testing cost by 50% to 86% with HCV viremia prevalence of 1% to 5% and various pooled sizes despite compromised pooled sensitivity. This pooled plasma HCV RNA testing strategy is cost-saving to identify acute HCV infections in high-risk populations with HCV viremia prevalence of 1% to 5%. IMPORTANCE Our three-stage pooled plasma HCV RNA testing successfully identified HCV viremia in high-risk PLWH with a testing cost reduction of 84.5%. Simulation analysis offered detailed information regarding the selection of pool and mini-pool sizes in settings of different HCV epidemiology and the performance of HCV RNA testing to optimize the cost reduction.

A fuzzy ontological knowledge document clustering methodology.

This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s) for given patents based on their derived ontological semantic webs. Finally, three case studies are used to test the approach. The first test case analyzed and clustered 100 patents for chemical and mechanical polishing retrieved from the World Intellectual Property Organization (WIPO). The second test case analyzed and clustered 100 patent news articles retrieved from online Web sites. The third case analyzed and clustered 100 patents for radio-frequency identification retrieved from WIPO. The results show that the fuzzy ontology-based document clustering approach outperforms the K-means approach in precision, recall, F-measure, and Shannon's entropy.