A total of 204 patients with liver biopsy-proven hepatitis C virus (HCV) infection, 84 with and 120 without human immunodeficiency virus (HIV) coinfection, were studied, to evaluate variables possibly associated with the stage of liver fibrosis. All patients were injection drugs users, with a mean age of 32 years and an estimated duration of HCV infection of 12 years. Twenty-four patients (11%) had many fibrous septa with (5%) or without (6%) cirrhosis, 56 (27%) had few fibrous septa, and 124 (60%) had no fibrous septa. In all patients, an association was found between CD4 cell counts <500 cells/mm(3)and the presence of many fibrous septa (odds ratio, 3.2; P=.037), independent of HIV infection and other factors. These results suggest that HIV infection-induced CD4 depletion is independently associated with the severity of liver fibrosis in chronic HCV infection.

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In order to assess the relationship between human immunodeficiency virus (HIV) RNA, hepatitis C virus (HCV) RNA, CD4, CD8, and liver enzymes during combination antiretroviral therapy, these parameters were measured in 12 HIV-HCV-coinfected patients (who were naive for antiretrovirals) on the day before and 3, 7, 14, 28, 56, and 84 days after initiating the following treatments: stavudine and lamivudine in all patients, indinavir in 6 patients, and nevirapine in 6 patients. HIV RNA declined rapidly, CD4 cells increased slowly, and CD8 cells and liver enzymes were stable. HCV RNA showed a transient significant increase at days 14 and 21 (7.33+/-0.16 [mean +/- SE] and 7.29+/-0.2 log copies/mL vs. 7+/-0.2 log copies/mL at baseline; P<.05). These changes were similar in both treatment groups. A 2-fold alanine aminotransferase increase was observed in 4 of 12 patients; 4 of 4 patients showed increased HCV RNA. The relationship between HCV RNA increase and HIV RNA decrease indicates virus-virus interference. An HCV RNA increase may cause significant liver damage only in a minority of patients.

The COrona VIrus Disease 19 (COVID-19) pandemic required the work of all global experts to tackle it. Despite the abundance of new studies, privacy laws prevent their dissemination for medical investigations: through clinical de-identification, the Protected Health Information (PHI) contained therein can be anonymized so that medical records can be shared and published. The automation of clinical de-identification through deep learning techniques has proven to be less effective for languages other than English due to the scarcity of data sets. Hence a new Italian de-identification data set has been created from the COVID-19 clinical records made available by the Italian Society of Radiology (SIRM). Therefore, two multi-lingual deep learning systems have been developed for this low-resource language scenario: the objective is to investigate their ability to transfer knowledge between different languages while maintaining the necessary features to correctly perform the Named Entity Recognition task for de-identification. The systems were trained using four different strategies, using both the English Informatics for Integrating Biology & the Bedside (i2b2) 2014 and the new Italian SIRM COVID-19 data sets, then evaluated on the latter. These approaches have demonstrated the effectiveness of cross-lingual transfer learning to de-identify medical records written in a low resource language such as Italian, using one with high resources such as English.