Fulminant hepatic failure in murine hepatitis virus strain 3 infection: tissue-specific expression of a novel fgl2 prothrombinase.

Activation of the immune coagulation system has been implicated in the pathogenesis of fulminant liver failure caused by murine hepatitis virus strain 3 (MHV-3). The recent discovery of the fgl2 gene, which encodes for MHV-3-induced prothrombinase (fgl2 prothrombinase), allows for fundamental studies to determine the molecular basis for fulminant liver failure. Transcription of the fgl2 gene and translation of the protein it encodes were examined in the liver and other organs of susceptible mice following MHV-3 infection. No constitutive expression of the fgl2 gene or the fgl2 prothrombinase was detected. Within 12 to 24 h of MHV-3 infection, however, fgl2 gene transcripts were detected in large amounts in the liver, spleen, and lungs, all of which are rich in reticuloendothelial cells, but were only focally present in small amounts in the kidney and brain. There was sequential detection of fgl2 prothrombinase in the liver, where it was localized specifically to the endothelium of intrahepatic veins and hepatic sinusoids; this was allowed by fibrin deposition, which resulted in confluent hepatocellular necrosis. These results provide further evidence for the role of the selective expression of this novel fgl2 prothrombinase in the pathogenesis of MHV-3-induced fulminant liver failure

Global prevalence and genotype distribution of hepatitis C virus infection in 2015 : A modelling study

Publisher Copyright: © 2017 Elsevier LtdBackground The 69th World Health Assembly approved the Global Health Sector Strategy to eliminate hepatitis C virus (HCV) infection by 2030, which can become a reality with the recent launch of direct acting antiviral therapies. Reliable disease burden estimates are required for national strategies. This analysis estimates the global prevalence of viraemic HCV at the end of 2015, an update of—and expansion on—the 2014 analysis, which reported 80 million (95% CI 64–103) viraemic infections in 2013. Methods We developed country-level disease burden models following a systematic review of HCV prevalence (number of studies, n=6754) and genotype (n=11 342) studies published after 2013. A Delphi process was used to gain country expert consensus and validate inputs. Published estimates alone were used for countries where expert panel meetings could not be scheduled. Global prevalence was estimated using regional averages for countries without data. Findings Models were built for 100 countries, 59 of which were approved by country experts, with the remaining 41 estimated using published data alone. The remaining countries had insufficient data to create a model. The global prevalence of viraemic HCV is estimated to be 1·0% (95% uncertainty interval 0·8–1·1) in 2015, corresponding to 71·1 million (62·5–79·4) viraemic infections. Genotypes 1 and 3 were the most common cause of infections (44% and 25%, respectively). Interpretation The global estimate of viraemic infections is lower than previous estimates, largely due to more recent (lower) prevalence estimates in Africa. Additionally, increased mortality due to liver-related causes and an ageing population may have contributed to a reduction in infections. Funding John C Martin Foundation.publishersversionPeer reviewe

The character of anti-HCV T cell responses differs between spontaneous and treatment-induced viral clearance s

Introduction: Hepatitis C virus (HCV) is a common infection with approximately 170 million individuals infected worldwide. The majority of exposed individuals develop chronic infection, with viral clearance being the exception to the rule. Even with antiviral therapy, only 50-75% of treated individuals become HCV RNA negative. The role of the adaptive immune response, and particularly T cells, has been well established in other viral infections but its function in HCV infection is unclear, making it difficult to develop either prophylactic or therapeutic vaccine strategies. Objectives: Compare the breadth, specificity, and magnitude of anti-HCV T cell responses in individuals who are chronically HCV-infected with those who clear HCV spontaneously or after antiviral treatment. Methods: We assessed T cell responses in 3 groups of HCV exposed individuals: 18 people who were chronically HCV infected (anti-HCV antibody and HCV RNA positive), 5 who were exposed to HCV but cleared the virus without treatment (antibody positive, RNA negative), and 17 who responded to anti-HCV treatment (antibody positive but RNA negative after treatment). CD4+ T cell responses to HCV and recall antigens were measured by standard 3H-thymidine proliferation assay, and CD8+ T cell responses were evaluated by incubating peripheral blood mononuclear cells (PBMC) overnight with 15-mer peptides spanning the entire HCV-1a genome. The number of HCV-specific interferon-gamma (IFN-g)-producing cells per million PBMC was estimated by ELISPOT. Results: Anti-HCV CD4+ T cell responses were uncommon, and of low magnitude in all groups. Responses were only to peptides in the core region. The overall breadth of the anti-HCV CD8+ response was greatest in treated responders (average 43/44 peptide pools tested positive) and least in those who failed treatment (average 11/44). All clearers had responses to the NS5 region. The magnitude of the CD8+ response tended to be highest in the treated responders, but there was no significant difference between groups. Conclusions: The character of the anti-HCV T cell response differs not just between chronic infection and clearance, but also between spontaneous versus treatment-associated HCV clearance. This suggests that it is likely a combination of T cell breadth, magnitude and specificity that results in viral clearance. Based on these results, development of therapeutic and prophylactic vaccine strategies may need to target different T cell responses

Omicron detection with large language models and YouTube audio data

Publicly available audio data presents a unique opportunity for the development of digital health technologies with large language models (LLMs). In this study, YouTube was mined to collect audio data from individuals with self-declared positive COVID-19 tests as well as those with other upper respiratory infections (URI) and healthy subjects discussing a diverse range of topics. The resulting dataset was transcribed with the Whisper model and used to assess the capacity of LLMs for detecting self-reported COVID-19 cases and performing variant classification. Following prompt optimization, LLMs achieved accuracies of 0.89, 0.97, respectively, in the tasks of identifying self-reported COVID-19 cases and other respiratory illnesses. The model also obtained a mean accuracy of 0.77 at identifying the variant of selfreported COVID-19 cases using only symptoms and other health-related factors described in the YouTube videos. In comparison with past studies, which used scripted, standardized voice samples to capture biomarkers, this study focused on extracting meaningful information from public online audio data. This work introduced novel design paradigms for pandemic management tools, showing the potential of audio data in clinical and public health applications