Background: HBV infection is a serious global heath problem. It is crucial to monitor this disease more closely with a non-invasive marker in clinical trials. We aimed to evaluate the predictive value of serum hyaluronate for the presence of extensive liver fibrosis and inflammation.

We report the case of an 11-year-old boy who presented with fresh blood in his mouth and a history of sore throat during the past 2 weeks that was unresponsive to antibiotic therapy. Inspection of the oral cavity revealed a black circular mass attached to the posterior wall of the oropharynx. This mass was determined to be a live engorged leech. Leech endoparasitism is caused by aquatic leeches that attach themselves to mucus membranes of body cavities after the host has swum in bodies of fresh water or drunk from unsafe water sources. Pharyngeal hirudiniasis may present in different forms depending on the exact location of the parasite in the body and must be included in the list of differential diagnosis for common conditions such as pharyngitis, hemoptysis or anemia in pediatric patients living in endemic areas.

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A multiprocessor scheduling problem is defined as the assignment of a given set of tasks to a set of processors. These tasks should be assigned in a way such that the total execution time is minimized and certain criteria are met. A wide range of solutions and heuristics have been proposed to solve this important system optimization problem. In this paper, we propose a novel representation to solve the task scheduling problem using genetic algorithm (GA). This representation is novel not only in the way it presents task scheduling, but also in that the length of that representation is intelligently adaptable to the given problem. Task duplication is allowed in our method and it is capable of spanning a large proportion of the solution space without the need for penalty/rewards or adding repair mechanisms whilst always generating valid chromosomes. Due to this new representation, order of the search space has been reduced; consequently, the proposed approach outperforms some recently studied GA based scheduling methods over 120 times with respect to the number of fitness evaluations.

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