Hepatitis C Virus Saint Petersburg Variant Detection With Machine Learning Methods.

Hepatitis C virus infection is a significant global health concern, affecting millions worldwide. Although direct-acting antivirals achieve over 90% success rate, treatment failures still occur, particularly when pan-genotypic DAAs are unavailable, and drugs need to be chosen based on the present HCV genotype. Genotyping tests can be misleading, especially in cases involving the 2k/1b recombinant variant.

Current methods for detecting and assessing HIV-1 antibody resistance.

Antiretroviral therapy is the standard treatment for HIV, but it requires daily use and can cause side effects. Despite being available for decades, there are still 1.5 million new infections and 700,000 deaths each year, highlighting the need for better therapies.

Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1.

Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables.

Torque Teno Virus Load Is Associated With Centers for Disease Control and Prevention Stage and CD4+ Cell Count in People Living With Human Immunodeficiency Virus but Seems Unrelated to AIDS-Defining Events and Human Pegivirus Load.

Background: Torque teno virus (TTV) is part of the human virome. TTV load was related to the immune status in patients after organ transplantation. We hypothesize that TTV load could be an additional marker for immune function in people living with HIV (PLWH).