Browsing by Subject "Anti-inflammatory agents"

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    Alcohol and medication interactions
    (U.S. National Institute on Alcohol Abuse and Alcoholism, 1999) Weathermon, Ron; Crabb, David W.; Medicine, School of Medicine
    Many medications can interact with alcohol, thereby altering the metabolism or effects of alcohol and/or the medication. Some of these interactions can occur even at moderate drinking levels and result in adverse health effects for the drinker. Two types of alcohol-medication interactions exist: (1) pharmacokinetic interactions, in which alcohol interferes with the metabolism of the medication, and (2) pharmacodynamic interactions, in which alcohol enhances the effects of the medication, particularly in the central nervous system (e.g., sedation). Pharmacokinetic interactions generally occur in the liver, where both alcohol and many medications are metabolized, frequently by the same enzymes. Numerous classes of prescription medications can interact with alcohol, including antibiotics, antidepressants, antihistamines, barbiturates, benzodiazepines, histamine H2 receptor antagonists, muscle relaxants, nonnarcotic pain medications and anti-inflammatory agents, opioids, and warfarin. In addition, many over-the-counter and herbal medications can cause negative effects when taken with alcohol.
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    Development and stability of IL-17-secreting T cells
    (2014) Glosson, Nicole L.; Kaplan, Mark H.; Blum, Janice Sherry, 1957-; Yu, Andy; Harrington, Maureen A.
    IL-17-producing T cells are critical to the development of pathogen and tumor immunity, but also contribute to the pathology of autoimmune diseases and allergic inflammation. CD8+ (Tc17) and CD4+ (Th17) IL-17-secreting T cells develop in response to a cytokine environment that activates Signal Transducer and Activator of Transcription (STAT) proteins, though the mechanisms underlying Tc17/Th17 development and stability are still unclear. In vivo, Tc17 cells clear vaccinia virus infection and acquire cytotoxic potential, that is independent of IL-17 production and the acquisition of IFN-γ-secreting potential, but partially dependent on Fas ligand, suggesting that Tc17-mediated vaccinia virus clearance is through cell killing independent of an acquired Tc1 phenotype. In contrast, memory Th cells and NKT cells display STAT4-dependent IL-23-induced IL-17 production that correlates with Il23r expression. IL-23 does not activate STAT4 nor do other STAT4-activating cytokines induce Il23r expression in these populations, suggesting a T cell-extrinsic role for STAT4 in mediating IL-23 responsiveness. Although IL-23 is important for the maintenance of IL-17-secreting T cells, it also promotes their instability, often resulting in a pathogenic Th1-like phenotype in vitro and in vivo. In vitro-derived Th17 cells are also flexible when cultured under polarizing conditions that promote Th2 or Th9 differentiation, adopting the respective effector programs, and decreasing IL-17 production. However, in models of allergic airway disease, Th17 cells do not secrete alternative cytokines nor adopt other effector programs, and remain stable IL-17-secretors. In contrast to Th1-biased pro-inflammatory environments that induce Th17 instability in vivo, during allergic inflammatory disease, Th17 cells are comparatively stable, and retain the potential to produce IL-17. Together these data document that the inflammatory environment has distinct effects on the stability of IL-17-secreting T cells in vivo.
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    Predicting Relapsing-Remitting Dynamics in Multiple Sclerosis Using Discrete Distribution Models: A Population Approach
    (Public Library of Science, 2013-09-05) Velez de Mendizabal, Nieves; Hutmacher, Matthew M.; Troconiz, Iñaki F.; Goñi, Joaquín; Villoslada, Pablo; Bagnato, Francesca; Bies, Robert R.; Medicine, School of Medicine
    Background: Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics. Methods and findings: We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment. Conclusions: The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.