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      Polyphenols isolated from Acacia mearnsii bark with anti-inflammatory and carbolytic enzyme inhibitory activities

      , , , , ,
      Chinese Journal of Natural Medicines
      Elsevier BV

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          Abstract

          The present study was designed to characterize the polyphenols isolated from Acacia mearnsii bark crude extract (B) and fractions (B1-B7) obtained by high-speed counter-current chromatography (HSCCC) and evaluate their anti-inflammatory and carbolytic enzymes (α-glucosidase and α-amylase) inhibitory activities. Fractions B4, B5, B6, B7 (total phenolics 850.3, 983.0, 843.9, and 572.5 mg·g −1 , respectively; proanthocyanidins 75.7, 90.5, 95.0, and 44.8 mg·g −1 , respectively) showed significant activities against reactive oxygen species (ROS), nitric oxide (NO) production, and expression of pro-inflammatory genes interleukin-1β (IL-1β) and inducible nitric oxide synthase (iNOS) in a lipopolysaccharide (LPS)-stimulated mouse macrophage cell line RAW 264.7. All the extracts suppressed α-glucosidase and α-amylase activities, two primary enzymes responsible for carbohydrate digestion. A. mearnsii bark samples possessed significantly stronger inhibitory effects against α-glucosidase enzyme (IC 50 of 0.4–1.4 μg·mL −1 ) than the pharmaceutical acarbose (IC 50 141.8 μg·mL −1 ). B6 and B7 (IC 50 17.6 and 11.7 μg·mL −1 , respectively) exhibited α-amylase inhibitory activity as efficacious as acarbose (IC 50 15.4 μg·mL −1 ). Moreover, B extract, at 25 μg·mL −1 , significantly decreased the non-mitochondrial oxidative burst that is often associated with inflammatory response in human monocytic macrophages.

          Author and article information

          Journal
          Chinese Journal of Natural Medicines
          Chinese Journal of Natural Medicines
          Elsevier BV
          18755364
          November 2017
          November 2017
          : 15
          : 11
          : 816-824
          Article
          10.1016/S1875-5364(18)30015-3
          47eae460-c832-4a19-b689-764698dd5f0d
          © 2017

          http://www.elsevier.com/tdm/userlicense/1.0/

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