Hydroxychloroquine (HQC) and chloroquine drugs belong to a class of drugs known as 4-aminoquinoline, its structure weak bases due to the presence of the essential side chain, and this chain contributes to the accumulation of drugs in the intracellular parts. A 21 mice were taken and divided into three groups, the first group (A) was the control group that administered oral distilled water for 30 days, and the second group (B) treated group that was dose with 15 mg/kg/day of drug for 30 days, and the third group (C) was the treated group by injected drug with a concentration of 30 mg/kg/day for 30 days also. The result of amino acids studied in the kidney of adult white mice (Mus musculus) showed the presence of (18) amino acid represented: asparagine (Asn), alanine (Ala), arginine (Arg), citrulline (Cit), glutamine (Glu), glycine (Gly), histidine (His), isoleucine (Ile), leucine (Leu), lysine (Iys), methionine (Met), proline (Pro), phenylalanine (Phe), serine (Ser), threonine (Thr), taurine (Tau), tyrosine (Tyr) and valine (Val). Statistical analysis showed high significant differences in the concentration of amino acids between the two groups of experiments treated with the drug (HQC) with a concentration (15 and 30) mg/kg/day and control group, as well as significant differences between the three groups.
We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
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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