Gene/Protein
Disease
Symptom
Drug
Enzyme
Compound
Pivot Concepts:
Gene/Protein
Disease
Symptom
Drug
Enzyme
Compound
Target Concepts:
Gene/Protein
Disease
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Enzyme
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Query: UMLS:C0013395 (
dyspepsia
)
4,879
document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)
Piper sylvaticum
Roxb. is traditionally used by the indigenous people of tropical and subtropical countries like Bangladesh, India, and China for relieving the common cold or a variety of chronic diseases, such as asthma, chronic coughing, piles, rheumatic pain, headaches, wounds, tuberculosis,
indigestion
, and
dyspepsia
. This study tested anxiolytic and antioxidant activities by
in vivo
,
in vitro
, and
in silico
experiments for the metabolites extracted (methanol) from the leaves and stems of
P. sylvaticum
(MEPSL and MEPSS). During the anxiolytic evaluation analyzed by elevated plus maze and hole board tests, MEPSL and MEPSS (200 and 400 mg/kg, body weight) exhibited a significant and dose-dependent reduction of anxiety-like behavior in mice. Similarly, mice treated with MEPSL and MEPSS demonstrated dose-dependent increases in locomotion and CNS simulative effects in open field test. In addition, both extracts (MEPSL and MEPSS) also showed moderate antioxidant activities in DPPH scavenging and ferric reducing power assays compared to the standard, ascorbic acid. In parallel, previously isolated bioactive compounds from this plant were documented and subjected to a molecular docking study to correlate them with the pharmacological outcomes. The selected four major phytocompounds displayed favorable binding affinities to potassium channel and
xanthine oxidoreductase
enzyme targets in molecular docking experiments. Overall,
P. sylvaticum
is bioactive, as is evident through experimental and computational analysis. Further experiments are necessary to evaluate purified novel compounds for the clinical evaluation.
...
PMID:Comparative Study of
Piper sylvaticum
Roxb. Leaves and Stems for Anxiolytic and Antioxidant Properties Through in vivo, in vitro, and in silico Approaches. 3221 19