Current treatment for chronic hepatitis C is expensive, is often accompanied by burdensome side effects, and, sadly, fails in almost half of cases. The ability to predict such failures prior to treatment could save a great deal of pain and expense for the patient with HCV. In this issue of the JCI, Aurora and colleagues describe the development of genetic markers predictive of treatment response based on a study of viral sequence variation (see the related article beginning on page 225). Genome-wide covariation analyses of pretreatment virus sequences from 94 patients showed distinct patterns of mutations strongly associated with the ultimate success or failure of treatment. Such analyses suggest markers predictive of response to therapy and may lead to new insights into the underlying biology of hepatitis C.
Thomas S. Oh, Charles M. Rice
Usage data is cumulative from December 2023 through December 2024.
Usage | JCI | PMC |
---|---|---|
Text version | 193 | 22 |
130 | 22 | |
Figure | 55 | 0 |
Citation downloads | 53 | 0 |
Totals | 431 | 44 |
Total Views | 475 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.