Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency.

BACKGROUND: Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown. METHODS: Medical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection. RESULTS: Several metrics were used to evaluate the models' performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively. CONCLUSIONS: The models developed in our study enable the prediction of potential infectious pathogens that may affect the patients with secondary immunodeficiency caused by various aetiologies except for HIV infection, which will help clinicians make a timely decision on antibiotic use before microorganism culture results return.

Research progress on HIV-1 immune escape mechanisms.

Although highly active antiretroviral therapy has transformed HIV-1 infection into a manageable chronic disease, the development of an effective vaccine is still an important and challengeable research field of HIV-1 treatment. The challenge arises from an enormous diversity of HIV-1 strains and their rapid evolution ahead of effective immune responses. HIV-1 evasion from host immunity contributes to viral spread and pathogenesis, thus understanding the mechanisms of HIV-1 immune evasion is important. In this review, we summarized our present knowledge on the mechanisms how HIV-1 escapes immune responses. Such knowledge will help with the design of effective vaccines capable of inducing immune control of HIV-1.

Research Progress of HIV-1 Nef Inhibitors.

HIV-1 infection poses a major threat to the public health worldwide. The antiretroviral agents that are currently used to treat HIV-1 infection target viral reverse transcriptase, integrase and protease, or block the fusion of viral envelop and cell membrane. Studies have shown that the HIV-1 encoded protein Nef plays an important role in the pathogenesis of viral infection. Nef ensures efficient counterattack against host immune responses as well as long-term evasion of immune surveillance. In addition, Nef, expressing at a high level early in the viral life cycle, is required for maintaining a high viral load in the persistent infection in vivo and for full pathologic potential. Therefore, Nef may be an excellent target to treat HIV-1 infection. In this manuscript, we reviewed five potential Nef inhibitors, namely, DLC27-14, t ightly bound hydroxypyrazole HIV-1 Nef inhibitor B9, 2c-like inhibitors, N-(3-aminoquinoxalin-2-yl)-4-chlorobenzenesulfonamide and compound 1[(7-oxo-7H-benzo[anthracene]-3-yl)amino]anthraquinone, and their working mechanisms. These drugs may be further developed into new regimens for the treatment of HIV-1 infection.