Leishmaniasis a vector- borne disease caused by obligate intra -macrophage protozoa, is characterized by diversity and complexity. Leishmania are one of different genera within the family Trypanosomatidae. Visceral leishmaniasis occurs universally, but >90% of the cases are in five countries: north-eastern India, Bangladesh, and Nepal in the Indian subcontinent, Sudan in Africa and north-eastern Brazil in South America. Sodium stibogluconate (Sb) has become ineffective in the 1990’s in most of the high -burden areas and must be replaced. However, none of the traditional alternatives was satisfactory. Oral drugs are very suitable as the need for hospitalization and related costs are eliminated, home handling is possible, coverage and access is better. Miltefosine (hexadecylphosphocholine, HePC), was the first oral drug that has proved to be highly effectual against VL, including antimony-resistant cases. This study tried to show the susceptibility of HePC on the treatment of liver tissues infected with VL in comparison to Sb. The results showed that HePC had a significant effect in treating VL infected liver tissues compared with those treated with Sb, which didn’t revealed any pronounced improvement in all used concentrations. On the contrary, it led to deterioration of infected liver tissues in when used in high concentrations.
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Industrial development has recently increased, including that of plastic industries. Since plastic has a very long analytical life, it will cause environmental pollution, so studies have resorted to reusing recycled waste plastic (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, producing environmentally friendly load-bearing concrete masonry units (blocks) was considered where five concrete mixtures were compressed at the blocks producing machine. The cement content reduced from 400 kg/m3 (B-400) to 300 kg/m3 (B-300) then to 200 kg/m3 (B-200). While (B-380) was produced using 380 kg/m3 cement and 20 kg/m3 nano-sil
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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