Search

Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered by the complexity of their interactions with the human host. This study provides an innovative approach for the identification and analysis of epidemiological associations between HIV Viral Infectivity Factor (Vif) protein mutations and four clinical endpoints (Viral load and CD4 T cell numbers at time of both clinical debut and on historical follow-up of patients. Furthermore, this study highlights an alternative approach to the analysis of imbalanced datasets, where patients without specific mutations outnumber those with mutations. Imbalanced datasets are still a challenge hindering the development of classification algorithms through machine learning. This research deals with Decision Trees, Naïve Bayes (NB), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). This paper proposes a new methodology considering an undersampling approach to deal with imbalanced datasets and introduces two novel and differing approaches (MAREV-1 and MAREV-2). As theses approaches do not involve human pre-determined and hypothesis-driven combinations of motifs having functional or clinical relevance, they provide a unique opportunity to discover novel complex motif combinations of interest. Moreover, the motif combinations found can be analyzed through traditional statistical approaches avoiding statistical corrections for multiple tests.

The Human Immunodeficiency Virus (HIV) Viral Infectivity Factor (Vif) is a 192-amino acid accessory protein essential to viral replication which counteracts host APOBEC3 proteins. APOBEC3 proteins interfere with the replication of HIV, hepatitis C virus, hepatitis B virus and retrotransposons. Vif is a recent candidate target for therapeutic and preventative interventions in HIV/AIDS yet little is known about its clinical relevance. We describe the results of applying different machine learning algorithms (Apriori, Multifactor Dimensionality Reductor, C4.5, Artificial Neural Networks and ID3) to the search of associations between HIV-1 Vif protein attributes and clinical endpoints. Final iterations showed that the presence of mutations in BC Boxes, APOBEC motifs and Cullin5 binding motifs were together associated with higher initial CD4 T cells while mutations of specific APOBEC motifs coupled with the conservation of other APOBEC motifs were associated with lower historic CD4 T cells. Conservation of specific APOBEC motifs and BC boxes were linked to lower initial viral loads while different combinations of mutations in the Nuclear Localisation Inhibition Signal and BC Boxes were associated with higher historic viral loads. Further scrutiny of these combinations through traditional statistical methods revealed striking differences in both CD4 T cells and viral loads in patients stratified into those having the previous combinations. While artificial intelligence algorithms do not phase out traditional statistical methods, our Artificial Intelligence (AI)-based approach highlights their use at reducing the dimensionality of large and complex datasets and at proposing novel, unimaginable, associations of biological patterns with functional relevance or clinical roles.

In 2016 WHO member states endorsed the 69th World Health Assembly's Global Health Sector Strategies (GHSS) towards eliminating Hepatitis B (HBV) infections by 2030. Substantial progress has been made in Mexico regarding HBV vaccination, blood safety and health-care setting injection safety but minor progress has been identified regarding timely HBV birth-dose coverage, access to diagnostic testing and treatment. Most importantly, Mexico's lack of a national plan for the fight against viral hepatitis was identified as a major obstacle for the development and implementation of actions and procuring funding. Insight of these deficiencies, we propose six actions that are in line with GHSS strategic directions to better allow Mexico to reach the goal of eliminating viral hepatitis by 2030. 1) the creation of a National Viral Hepatitis Task Group capable of providing direction, recommendations as well as innovative solutions in the fight against viral hepatitis in Mexico; 2) the drafting of a National Plan for viral hepatitis; 3) establishing a national viral hepatitis information database; 4) development of Clinical Practice Guidelines; 5) appeal for governmental responsibility and accountability; 6) promote basic, applied science projects as well as clinical research to determine the viral, epidemiological, genomic, ethnic and cultural peculiarities of viral hepatitis infections in Mexico. These basic actions will better equip Mexico to meet GHSS targets, lead to high-impact health interventions and ultimately enhance the cascade of care, from diagnosis to chronic care. Political commitment is a requirement to the implementation of these actions but civil society involvement is also seen to be crucial.

We describe the clinical characteristics and outcomes of adults hospitalized with pneumonia during the pandemic (H1N1) 2009 outbreak. Patients admitted to a general hospital in San Luis Potosí, Mexico, from April 10 through May 11, 2009, suspected to have influenza virus–associated pneumonia were evaluated. We identified 50 patients with suspected influenza pneumonia; the presence of influenza virus was confirmed in 18: 11 with pandemic (H1N1) 2009 virus, 5 with unsubtypeable influenza A virus, 1 with seasonal influenza A virus (H3N2), and 1 in whom assay results for seasonal and pandemic (H1N1) 2009 viruses were positive. Eighteen patients were treated in the intensive care unit, and 10 died. During the pandemic (H1N1) 2009 outbreak, severe pneumonia developed in young adults who had no identifiable risk factors; early diagnosis and treatment of influenza virus infections may have a determinant role in outcome.