Prevalence of Spontaneous Clearance of Hepatitis C Virus Infection Doubled From 1998 to 2017

Seo, Suk; Silverberg, Michael J.; Hurley, Leo B.; Ready, Joanna; Saxena, Varun; Witt, David J.; Hare, C. Bradley; Champsi, Jamila H.; Korn, Daniel G.; Pauly, Mary Pat; Chamberland, Scott; Lai, Jennifer B.; Marcus, Julia L.

doi:10.1016/j.cgh.2019.04.035

Cited by 30 publications

(29 citation statements)

References 7 publications

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“…Historically, approximately 15%-25% of persons were believed to resolve their acute infection without sequelae (30); however, more recent data suggest that spontaneous clearance might be as high as 46%, varying by age at the time of infection (31). Spontaneous clearance is lower among persons co-infected with HIV (11).…”

Section: Clinical Features and Natural Historymentioning

confidence: 99%

CDC Recommendations for Hepatitis C Screening Among Adults — United States, 2020

et al. 2020

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Section: Clinical Features and Natural Historymentioning

confidence: 99%

CDC Recommendations for Hepatitis C Screening Among Adults — United States, 2020

et al. 2020

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“…After that, CFS-selected feature vectors of the sequences were used as input data to train a stochastic gradient descent (SGD) classifier ( Zhang 2004 ; Seo et al. 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…One of the most important questions is the relative contribution of random and deterministic evolutionary factors in disease progression or, using the metaphor of Gould (1990) , whether it is possible to ‘replay the tape of life’ for the virus evolution inside a host. This question is of high importance for biomedical research, as predictability of viral evolution potentially implies the power to understand and control the disease ( Lassig, Mustonen, and Walczak 2017 ; Seo et al. 2020 ), which may result in advanced diagnostic and treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections

et al. 2020

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Detection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution. We studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected features measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties. Distributions of the viral population features differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a machine learning classifier for the infection staging, which yielded a detection accuracy of 95.22 per cent, thus providing a higher accuracy than other genomic-based models. The detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, which could potentially complement and/or substitute standard laboratory assays.

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“…Gould 7 , whether is it possible to "replay the tape of life" for the virus evolution inside a host. This question is of high importance for biomedical research, as predictability of viral evolution potentially implies the power to understand and control the disease 8,9 , which may result in advanced diagnostic and treatment strategies. In this paper, we study evolutionary factors associated with the transition between HCV infection stages.…”

Section: Introductionmentioning

confidence: 99%

Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections

Baykal

Lara

Khudyakov

et al. 2020

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Background Detection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution.Methods We studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected parameters measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties.Findings Distributions of the viral population parameters differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a Machine Learning classifier for the infection staging, which yielded a detection accuracy of 95.22%, thus providing a higher accuracy than other genomic-based models.Interpretation The detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, that could potentially complement and/or substitute standard laboratory assays.

show abstract

Prevalence of Spontaneous Clearance of Hepatitis C Virus Infection Doubled From 1998 to 2017

Cited by 30 publications

References 7 publications

CDC Recommendations for Hepatitis C Screening Among Adults — United States, 2020

CDC Recommendations for Hepatitis C Screening Among Adults — United States, 2020

Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections

Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections

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