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.     

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.     

Ultrastructural comparison of developing mouse embryonic stem cell- and in vivo-derived cardiomyocytes

Embryonic stem cells (ESCs) are expected to become a powerful tool for future regenerative medicine and developmental biology due to their capacity for self-renewal and pluripotency. The present study involves characterization and particularly, the ultrastructure of ESC-derived cardiomyocytes (ESC-CMs). Spontaneously differentiated murine (C57BL/6) ESC-CMs were cultured for 21 days. At different stages, growth characteristics of the CMs were assessed by immunocytochemistry, RT-PCR, transmission electron microscopy, and by addition of chronotropic drugs. EB-derived spontaneously beating cells expressed markers characteristic of CMs including alpha-actinin, desmin, troponin I, sarcomeric myosin heavy chain (MHC), pan-cadherin, connexin 43, cardiac alpha-MHC, cardiac beta-MHC, atrial natriuretic factor (ANF), and myosin light chain isoform-2V (MLC-2V) and responded to drugs in a maturation- and dose-dependent manner. At the ultrasructural level, maturation proceeded with increasing time in culture. In 7+21 days CMs, all sarcomeric components, such as Z-discs, A-, I- and H-bands as well as M-lines, T-tubules, intercalated discs, and the sarcoplasmic reticulum were present. Our data suggest that ESCs can differentiate into functional mature CMs in vitro. Furthermore, ESC-CMs may provide an ideal model for the study of cardiomyocytic development and may be useful for cell therapy of various cardiac diseases.     

Resolution of sertraline with (R)-mandelic acid: chiral discrimination mechanism study

The chiral discrimination mechanism in the resolution of sertraline with mandelic acid was investigated by examining the weak intermolecular interactions (such as hydrogen bond, CH/π, and van der Waals interactions) and molecular packing difference in crystal structures of the resulting diastereomeric salts. A new one-dimensional chain-like hydrogen-bonding network and unique supramolecular packing mode are disclosed. The investigation demonstrated that stable hydrogen-bonding pattern, herringbone-like arrangement of aromatic rings, and planar boundary surface in the hydrophobic region are the three most important structural characteristics expected in less soluble diastereomeric salts. The existence and magnitude of hydrogen bond, CH/π interaction, and van der Waals interaction related to three characteristic structures, determine the stability of diastereomeric salt. The hydrogen bond is not necessarily the dominant factor while the synergy and optimization of all weak intermolecular interactions attribute to the chiral recognition.     

DNA approach to solve clustering problem based on a mutual order

Clustering is regarded as a consortium of concepts and algorithms that are aimed at revealing a structure in highly dimensional data and arriving at a collection of meaningful relationships in data and information granules. The objective of this paper is to propose a DNA computing to support the development of clustering techniques. This approach is of particular interest when dealing with huge data sets, unknown number of clusters and encountering a heterogeneous character of available data. We present a detailed algorithm and show how the essential components of the clustering technique are realized through the corresponding mechanisms of DNA computing. Numerical examples offer a detailed insight into the performance of the DNA-based clustering.     

Natural flavonoids for the prevention of colon cancer: A comprehensive review of preclinical and clinical studies

Flavonoids comprise a group of natural polyphenols consisting of more than 5,000 subtypes mostly existing in fruits and vegetables. Flavonoids consumption could potentially attenuate the incidence and recurrence risk of colorectal cancers through their antiperoxidative, antioxidant, and anti-inflammatory effects. In addition, these compounds regulate the mitochondrial function, balance the bacterial flora and promote the apoptosis process in cancerous cells. However, some previous data failed to show the effectiveness of flavonoids in reducing the risk of colorectal cancer. In this study, we have reviewed the efficacy of different flavonoids subtypes on the risk of colon cancer and molecular mechanisms involved in this process in both clinical and animal studies. In addition, we tried to elucidate the potential synergy between these compounds and current colorectal cancer treatments.