Effect of Antioxidants in Cathepsin B Release by HIV Infected Macrophages

During HIV infection of macrophages, the lysosomal protein cathepsin B is released and induces neurotoxicity. Also, the levels of cathepsin B are increased in plasma and post-mortem brain tissue of patients with HIV-associated dementia. Oxidative damage is increased in HIV- infected patients, while antioxidants are decreased in HIV-associated dementia. Dimethyl fumarate (DMF), an antioxidant, has been reported to decrease HIV replication and neurotoxicity caused by HIV-infected macrophages. Since HIV also increases cathepsin B, we hypothesize that DMF will also reduce cathepsin B release from HIV-infected macrophages. Monocyte-derived macrophages (MDM) were isolated from healthy donors and inoculated with HIV-1ADA. After removal of infection, MDM were treated with DMF at different concentrations (15, 30, and 60 µM) until day 12 post-infection, changing and collecting media every three days. HIV-1p24 and cathepsin B levels were assessed from HIV-infected MDM supernatants at the end of cultures using ELISA. Results indicate that DMF reduced HIV-1 replication and cathepsin B secretion from HIV-infected macrophages, in a concentration-dependent manner, in comparison with vehicle (DMSO)-treated controls. However, cathepsin B secretion was not affected by HIV infection in vehicle-treated controls. In conclusion, DMSO may have had an unexpected effect in cathepsin B secretion in our experiments, and this could explain why cathepsin B secretion was not affected by HIV infection. Future experiments will include an untreated control group to determine if DMSO vehicle is having an effect in cathepsin B secretion. This will lead us to determine the role of DMF in cathepsin B secretion from HIV-infected macrophages

Childhood Sexual Abuse in Boys Under the Age of 18: Nonverbal Disclosure Patterns Through Behavior

Adverse childhood experiences (ACE) include being exposed to or victimized by physical, emotional, or sexual abuse; if left untreated, traumas may disrupt normative biopsychosocial development. Childhood sexual abuse (CSA) is a phenomenon investigated by many researchers, however, the scientific community suspects CSA in boys is underreported. Since there are significant gender differences in CSA verbal disclosures, examining nonverbal disclosures in boys through overt, sexualized behaviors may help clinicians confirm suspected cases of CSA. Since most research investigates verbal disclosures of sexual abuse, this study will investigate nonverbal disclosure in boys between the ages of 6 through 10 using naturalistic observations in three intensive group homes (IGH) in the northeastern United States. An IGH is an out-of-home placement that aims to shelter children from their abusers and help children heal through their trauma. Since early intervention treatments are shown to be effective with children with sexual abuse backgrounds, it is important to understand how sexualized behavior indicative of children with a CSA history to reduce the impacts of trauma on healthy childhood development. Early intervention techniques are effective when treating children and adolescents with a history of CSA and ACE. This research will examine boys with suspected CSA histories between the ages of 6 through 10 in an IGH setting

Maximal information component analysis: a novel non-linear network analysis method.

BackgroundNetwork construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.ResultsWe have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.ConclusionsIn making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions

Biomimetic approaches to engineer bioactive glass-based nanosystems

Despite the remarkable osteoconductive properties attributed to bioactive glass since its discovery, it is still a brittle material. Therefore, its applications are limited by a proper engineering of the material’s structure, or by its combination with other materials, like polymers. In native mineralized tissues, the blend of organic with inorganic phases is frequently the key for the remarkable mechanical properties of this class of natural materials.The main goal of this work was to give a step further in producing in vitro materials able to mimic the structural and chemical environment necessary to bone growth. Micro and nanofabrication techniques were used to recapitulate the complex environment of mineralized tissues. Bioactive glass and chitosan were chosen as materials to be combined respectively as mineral and organic phase in order to mimic bone structure