Tuberculosis (TB), caused by Mycobacterium tuberculosis (M. tb), continues to be one of the most prevalent infectious diseases in the world. There is an upward trend in occurrence due to emerging multidrug resistant strains and an increasingly larger proportion of immunocompromised patient populations as a result of the acquired immunodeficiency syndrome (AIDS) pandemic. The complex and often deadly combination of multidrug resistant M. tb (MDR-M. tb) along with human immunodeficiency virus (HIV) puts a significant number of people at high risk for pulmonary and extra-pulmonary TB without sufficient therapeutic options available. Natural Killer (NK) cells and macrophages are major components of the body’s innate immune system, contributing significantly to the body’s ability to synergistically inhibit the growth of M. tb in immune compromised individuals lacking a sufficient T cell response. Direct mechanisms of control are largely through the secretory products perforin, granulysin and granzymes, as well as multiple membrane-bound death receptors that facilitate target directed lysis. NK cells also have a role in indirectly stimulating an immune response through activation of macrophages and monocytes with multiple signaling pathways including both reactive oxygen species (ROS) and reactive nitrogen species (RNS). Glutathione (GSH) has been shown to play a part in inhibiting growth of intracellular M. tb through bacteriostatic mechanisms. Enhancing cellular GSH through several cytokines and N-acetyl cysteine (NAC) has been shown to increase these effects, at least in part, through their action on NK cells. Taken overall, there is substantial evidence for a mechanistic correlation between NK cell activity and functionality in combating M. tb in HIV infection mediated through adequate GSH production and use.

RhoB, a member of the Ras homolog gene family and GTPase, regulates intracellular signaling pathways by interfacing with epidermal growth factor receptor (EGFR), Ras, and phosphatidylinositol 3-kinase (PI3K)/Akt to modulate responses in cellular structure and function. Notably, the EGFR, Ras, and PI3K/Akt pathways can lead to downregulation of RhoB, while simultaneously being associated with an increased propensity for tumorigenesis. Functionally, RhoB, part of the Rho GTPase family, regulates intracellular signaling pathways by interfacing with EGFR, RAS, and PI3K/Akt/mammalian target of rapamycin (mTOR), and MYC pathways to modulate responses in cellular structure and function. Notably, the EGFR, Ras, and PI3K/Akt pathways can lead to downregulation of RhoB, while simultaneously being associated with an increased propensity for tumorigenesis. RHOB expression has a complex regulatory backdrop consisting of multiple histone deacetyltransferase (HDACs 1 and 6) and microRNA (miR-19a, -21, and -223)-mediated mechanisms of modifying expression. The interwoven nature of RhoB’s regulatory impact and cellular roles in regulating intracellular vesicle trafficking, cell motion, and the cell cycle lays the foundation for analyzing the link between loss of RhoB and tumorigenesis within the context of age-related decline in RhoB. RhoB appears to play a tissue-specific role in tumorigenesis, as such, uncovering and appreciating the potential for restoration of RHOB expression as a mechanism for cancer prevention or therapeutics serves as a practical application. An in-depth assessment of RhoB will serve as a springboard for investigating and characterizing this key component of numerous intracellular messaging and regulatory pathways that may hold the connection between aging and tumorigenesis.

Introduction: Atrial fibrillation is the most common postoperative arrhythmia and is associated with increased length of stay, cost, morbidity and mortality. The incidence of postoperative atrial fibrillation for noncardiac, nonthoracic surgeries ranges from 0.4% to 26%. The incidence increases to 20%–50% in cardiac surgery, occurring in approximately 30% of isolated coronary artery bypass grafting (CABG), approximately 40% of isolated valve surgeries and up to 50% of CABG plus valve surgeries. Our aim was to identify risk factors that may predispose patients to postoperative atrial fibrillation and compare the efficacy of previously developed prediction tools to a new bedside prediction tool. We sought to develop a bedside screening tool using 4 easily identifiable variables: body mass index, age, congestive heart failure and hypertension (BACH). We predicted that our model would compare similarly to previously developed and validated prediction models but would be easier to use. Methods: We retrospectively identified 672 patients without a history of atrial fibrillation who had undergone cardiac surgery from July 2011 to December 2018. The risk factors for atrial fibrillation were evaluated alongside previously developed prediction tools. Using logistic regression, t tests and receiver operator characteristic (ROC) analysis, we compared previously used risk stratification scores of CHA2DS2-VASc, CHARGE-AF and age. We also compared our proposed BACH risk prediction tool to our population and compared it against CHA2DS2-VASc, CHARGE-AF and age. In a subpopulation analysis of 259 people, we evaluated if left atrial size was an independent risk factor for the development of postoperative atrial fibrillation. Results: A total of 131 patients—approximately 20%—developed postoperative atrial fibrillation. CHA2DS2-VASc had the lowest area under the curve (AUC) and did not perform as well at classifying patients with postoperative atrial fibrillation as the other 3 predictors. CHARGE-AF, age by itself and age per 5 years performed relatively similarly to one another. ROC was greatest for age alone (ROC area .634, 95% CI: .581–.688), followed by CHARGE-AF (ROC area .631, 95% CI: .577–.684), and finally CHA2DS2-VASc (ROC area .564, 95% CI: .509–.619). A logistic model was fit for the BACH variables (continuous versions of body mass index, age, congestive heart failure and hypertension). The model achieved good fit, χ2(671, N=672)=633.029, P=.816, Nagelkerke R2=.070. However, only the predictors of age and prior heart failure were found to be significant. For BACH, the C-statistic (and AUC) for the model was .645 (95% CI: .601, .707), which was marginally better than age alone. All the models that were fit using ROC analyses were not statistically different from one another in terms of performance. No statistical significance was found between the 2 groups for preoperative left atrial size. Conclusion: These findings suggest that age may be the highest risk factor for postoperative atrial fibrillation. The bedside prediction tool BACH compared slightly better than age alone but was not statistically different from the other prediction tools’ performance. The BACH prediction tool is easy to use, includes only 4 factors that are readily available at the bedside and improves prediction over age alone.