Apple vinegar has many uses that include burn and wound healing and as an antimicrobial agent against different microorganisms, but not as a solvent. Therefore, this study aimed to use commercial apple vinegar as solvent to the plants of roselle (Hibiscus sabdariffa), green tea (Camellia sinensis), and clove (Syzygium aromaticum). The effects of apple-vinegar extracts of these plants were compared with those of aqueous and ethanolic extracts against biofilm formation by Candida genus. Clove vinegar extract demonstrated antibiofilm activity against C. albicans, alone (2.4907± 0.382) or in combination with the antifungal agents fluconazole (1.689±0.33), nystatine (1.941±0.64), and clotrimazole (2.0353±0.71819). These plant extracts possessed a variable number of antimicrobial compounds, as tested by the HLPC technique. Therefore, apple vinegar was the most efficient solvent, in comparison with the other solvents used in this study, to obtain some phytochemical compounds from the tested plants that have antibiofilm activity against C. albicans.
Coumarins have been recognized as anticancer competitors. HDACis are one of the interesting issues in the field of antitumor research. In order to achieve an increased anticancer efficacy, a series of hybrid compounds bearing coumarin scaffolds have been designed and synthesized as novel HDACis, In this review we present a series of novel HDAC inhibitors comprising coumarin as a core e of cap group of HDAC inhibitors that have been designed, synthesized and assessed for their enzyme inhibitory activity as well as antiproliferative activity. Most of them exhibited potent HDAC inhibitory activity and significant cytotoxicity
Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto
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