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.     

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.     

Similar hypotensive responses to resistance exercise with and without blood flow restriction

Low intensity resistance exercise (RE) with blood flow restriction (BFR) has gained attention in the literature due to the beneficial effects on functional and morphological variables, similar to those observed during traditional RE without BFR, while the effects of BFR on post-exercise hypotension remain unclear. The aim of the present study was to compare the blood pressure (BP) response of trained normotensive individuals to RE with and without BFR. In this cross-over randomized trial, eight male subjects (23.8 ± 4 years, 74 ± 3 kg, 174 ± 4 cm) completed two exercise protocols: traditional RE (3 x 10 repetitions at 70% one-repetition maximum [1-RM]) and low intensity RE (3 x 15 repetitions at 20% 1-RM) with BFR. Blood pressure measurements were performed after 15 min of seated rest (0), immediately after and 10 min, 20 min, 30 min, 40 min, 50 min and 60 min after the experimental sessions. Similar hypotensive effects for systolic BP (SBP) were observed for both protocols (P < 0.05) after exercise, with no differences between groups (P > 0.05) and no statistically significant difference for diastolic BP (P > 0.05). These results suggest that in normotensive trained individuals, both traditional RE and RE with BFR induce hypotension for SBP, which is important to prevent cardiovascular disturbances.     

Long-term treadmill exercise improves spatial memory of male APPswe/PS1dE9 mice by regulation of BDNF expression and microglia activation

Increasing evidence suggests that physical activity could delay or attenuate the symptoms of Alzheimer''s disease (AD). But the underlying mechanisms are still not fully understood. To investigate the effect of long-term treadmill exercise on the spatial memory of AD mice and the possible role of β-amyloid, brain-derived neurotrophic factor (BDNF) and microglia in the effect, male APPswe/PS1dE9 AD mice aged 4 months were subjected to treadmill exercise for 5 months with 6 sessions per week and gradually increased load. A Morris water maze was used to evaluate the spatial memory. Expression levels of β-amyloid, BDNF and Iba-1 (a microglia marker) in brain tissue were detected by immunohistochemistry. Sedentary AD mice and wildtype C57BL/6J mice served as controls. The results showed that 5-month treadmill exercise significantly decreased the escape latencies (P < 0.01 on the 4th day) and improved the spatial memory of the AD mice in the water maze test. Meanwhile, treadmill exercise significantly increased the number of BDNF-positive cells and decreased the ratios of activated microglia in both the cerebral cortex and the hippocampus. However, treadmill exercise did not significantly alleviate the accumulation of β-amyloid in either the cerebral cortex or the hippocampus of the AD mice (P > 0.05). The study suggested that long-term treadmill exercise could improve the spatial memory of the male APPswe/PS1dE9 AD mice. The increase in BDNF-positive cells and decrease in activated microglia might underpin the beneficial effect.     

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.     

Poverty trap formed by the ecology of infectious diseases

While most of the world has enjoyed exponential economic growth, more than one-sixth of the world is today roughly as poor as their ancestors were many generations ago. Widely accepted general explanations for the persistence of such poverty have been elusive and are needed by the international development community. Building on a well-established model of human infectious diseases, we show how formally integrating simple economic and disease ecology models can naturally give rise to poverty traps, where initial economic and epidemiological conditions determine the long-term trajectory of the health and economic development of a society. This poverty trap may therefore be broken by improving health conditions of the population. More generally, we demonstrate that simple human ecological models can help explain broad patterns of modern economic organization.