The Role of Peptide-Based Tumor Vaccines on Cytokines of Adaptive Immunity: A Review

International Journal of Peptide Research and Therapeutics - Recently, peptide-based materials have been applied to solving many therapeutic problems and have shown particular efficacy as cancer...     

Hepatoprotective Effect of Manganese Chloride Against CCl4-Induced Liver Injury in Rats

The aim of the present study is to evaluate the protective effect of manganese chloride against carbon tetrachloride (CCl₄)-induced liver injury in rats. Manganese chloride (0.001, 0.01, 0.05 and 0.1 g/kg bw) was administered intragastrically for 28 consecutive days to male CCl₄-treated rats. The hepatoprotective activity was assessed using various biochemical parameters such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyltransferase (GGT) and superoxide dismutase (SOD). Histopathological changes in the liver of different groups were also studied. Administration of CCl₄ increased the serum ALT, AST, ALP and GGT but decreased SOD levels in rats. Treatment with manganese chloride significantly attenuated these changes to nearly normal levels. The animals treated with manganese chloride have shown decreased necrotic zones and hepatocellular degeneration when compared to the liver exposed to CCl₄ intoxication alone. Thus, the histopathalogical studies also supported the protective effect of manganese chloride. Therefore, the results of this study suggest that manganese chloride exerts hepatoprotection via promoting antioxidative properties against CCl₄-induced oxidative liver damage.     

Skeletons, noise and population growth: the end of an old debate?

Population dynamics models remain largely deterministic, although the presence of random fluctuations in nature is well recognized. This deterministic approach is based on the implicit assumption that systems can be separated into a deterministic part that captures the essential features of the system and a random part that can be neglected. But is it possible, in general, to understand population dynamics without the explicit consideration of random fluctuations? Here, we suggest perhaps not, and argue that the dynamics of many systems are a result of interactions between the deterministic nonlinear skeleton and noise.     

Cholera toxin B pretreatment of macrophages and monocytes diminishes their proinflammatory responsiveness to lipopolysaccharide

The cholera toxin B chain (CTB) has been reported to suppress T cell-dependent autoimmune diseases and to potentiate tolerance of the adaptive immune system. We have analyzed the effects of CTB on macrophages in vitro and have found that preincubation with CTB (10 microg/ml) suppresses the proinflammatory reaction to LPS challenge, as demonstrated by suppressed production of TNF-alpha, IL-6, IL-12(p70), and NO (p < 0.01) in cells of macrophage lines. Pre-exposure to CTB also suppresses LPS-induced TNF-alpha and IL-12(p70) formation in human PBMC. Both native and recombinant CTB exhibited suppressive activity, which was shared by intact cholera toxin. In cells of the human monocyte line Mono Mac 6, exposure to CTB failed to suppress the production of IL-10 in response to LPS. Control experiments excluded a role of possible contamination of CTB by endotoxin or intact cholera toxin. The suppression of TNF-alpha production occurred at the level of mRNA formation. Tolerance induction by CTB was dose and time dependent. The suppression of TNF-alpha and IL-6 production could be counteracted by the addition of Abs to IL-10 and TGF-beta. IFN-gamma also antagonized the actions of CTB on macrophages. In contrast to desensitization by low doses of LPS, tolerance induction by CTB occurred silently, i.e., in the absence of a measurable proinflammatory response. These findings identify immune-deviating properties of CTB at the level of innate immune cells and may be relevant to the use of CTB in modulating immune-mediated diseases.     

Atheroprotective and hepatoprotective effects of trans-chalcone through modification of eNOS/AMPK/KLF-2 pathway and regulation of COX-2, Ang-II,and PDGF mRNA expression in NMRI mice fed HCD

Molecular Biology Reports - The effects of trans-chalcone on atherosclerosis and NAFLD have been investigated. However, the underlying molecular mechanisms of these effects are not completely...     

Detection and characterization of mutations in genes related to isoniazid resistance in Mycobacterium tuberculosis clinical isolates from Iran

Molecular Biology Reports - The global rise in drug-resistant Mycobacterium tuberculosis (M.tb), and especially the significant prevalence of isoniazid (INH)-resistance constitute a significant...     

Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.     

Appropriate models for the management of infectious diseases

BackgroundMathematical models have become invaluable management tools for epidemiologists, both shedding light on the mechanisms underlying observed dynamics as well as making quantitative predictions on the effectiveness of different control measures. Here, we explain how substantial biases are introduced by two important, yet largely ignored, assumptions at the core of the vast majority of such models.ConclusionThis work aims to highlight that, when developing models for public health use, we need to pay careful attention to the intrinsic assumptions embedded within classical frameworks.