Honokiol (HNK) is a natural polyphenolic compound extracted from the bark and leaves of It has been traditionally used as a medicinal compound to treat inflammatory diseases. HNK possesses numerous health benefits with a minimal level of toxicity. It can cross the blood-brain barrier and blood-cerebrospinal fluid, thus having significant bioavailability in the neurological tissues. HNK is a promising bioactive compound possesses neuroprotective, antimicrobial, anti-tumorigenic, anti-spasmodic, antidepressant, analgesic, and antithrombotic features . HNK can prevent the growth of several cancer types and haematological malignancies. Recent studies suggested its role in COVID-19 therapy. It binds effectively with several molecular targets, including apoptotic factors, chemokines, transcription factors, cell surface adhesion molecules, and kinases. HNK has excellent pharmacological features and a wide range of chemotherapeutic effects, and thus, researchers have increased interest in improving the therapeutic implications of HNK to the clinic as a novel agent. This review focused on the therapeutic implications of HNK, highlighting clinical and pharmacological features and the underlying mechanism of action.Communicated by Ramaswamy H. Sarma.
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http://dx.doi.org/10.1080/07391102.2022.2120541 | DOI Listing |
J Org Chem
December 2024
Department of Pharmacy, The First Affiliated Hospital, Jinan University, Guangzhou 510630, China.
Nine new structurally diverse filicinic acid-based meroterpenoids (-) with four kinds of carbon skeletons were isolated from the rhizomes of . Their structures, including the absolute configurations, were elucidated by comprehensive analysis of spectroscopic data, quantum chemical calculations, and single-crystal X-ray diffraction. Structurally, compounds - feature an unprecedented 6/6/5/6/6/6 hexacyclic system with a rare oxaspiro[4.
View Article and Find Full Text PDFProtein Sci
January 2025
Department of Neuroscience, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
Human succinic semialdehyde dehydrogenase is a mitochondrial enzyme fundamental in the neurotransmitter γ-aminobutyric acid catabolism. It catalyzes the NAD-dependent oxidative degradation of its derivative, succinic semialdehyde, to succinic acid. Mutations in its gene lead to an inherited neurometabolic rare disease, succinic semialdehyde dehydrogenase deficiency, characterized by mental and developmental delay.
View Article and Find Full Text PDFPhytother Res
December 2024
Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, Benha University, Toukh, Egypt.
(1) Background and aim: Aloe arborescens Mill. (A. arborescens) is one of the most widely distributed species in the genus Aloe and has garnered widespread recognition for its anticancer properties.
View Article and Find Full Text PDFBehav Brain Funct
December 2024
Department of Pharmacology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan.
The large-conductance calcium- and voltage-activated potassium (BK) channels, encoded by the KCNMA1 gene, play important roles in neuronal function. Mutations in KCNMA1 have been found in patients with various neurodevelopmental features, including intellectual disability, autism spectrum disorder (ASD), or attention deficit hyperactivity disorder (ADHD). Previous studies of KCNMA1 knockout mice have suggested altered activity patterns and behavioral flexibility, but it remained unclear whether these changes primarily affect immediate behavioral adaptation or longer-term learning processes.
View Article and Find Full Text PDFSci Rep
December 2024
Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France.
Personalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared to the limited number of samples presents significant challenges. Knowledge-based feature selection methods are particularly suitable for drug response prediction, as they leverage biological insights to reduce dimensionality and improve model interpretability.
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