Beta-bitter acids of hops (lupulones) revealed sedative and antidepressant-like effects in animal studies. Transformation of β-acids during beer brewing leads to the formation of tricyclic transformation products, which have a close structural analogy to hyperforin. The latter compound is responsible for the antidepressant activity of St. John's wort by activation of TRPC6 cation channels in neuronal-like cells leading to Ca influx. In this study, nortricyclolupones, dehydrotricyclolupones, and tricyclolupones were isolated from a wort-boiling model and their structures were elucidated by UHPLC-DAD, UHPLC-ESI-MS/MS and 1D/2D-NMR spectroscopy. Beta-bitter acids and their transformation products induced Ca influx in PC12 cells to the same extent as hyperforin. Application of a Ca-free environment abolished the Ca elevation, indicating that the increase is mediated by influx across the plasma membrane. Thus, activation of neuronal-like Ca-channels by lupulones and tricyclolupones represent a novel mechanism contributing to the antidepressant activity of hops.
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http://dx.doi.org/10.1016/j.foodchem.2018.01.073 | DOI Listing |
Uterine fibroids (UFs) are the most common non-cutaneous tumors in women worldwide. UFs arise from genetic alterations in myometrial stem cells (MM SCs) that trigger their transformation into tumor initiating cells (UF SCs). Mutations in the RNA polymerase II Mediator subunit MED12 are dominant drivers of UFs, accounting for 70% of these clinically significant lesions.
View Article and Find Full Text PDFMost malignant hepatocellular tumors in children are classified as either hepatoblastoma (HB) or hepatocellular carcinoma (HCC), but some tumors demonstrate features of both HB and HCC . These tumors have been recognized under a provisional diagnostic category by the World Health Organization and are distinguished from HB and HCC by a combination of histological, immunohistochemical, and molecular features . Their outcomes and cellular composition remain an open question .
View Article and Find Full Text PDFAtherosclerosis, a major contributor to cardiovascular disease, involves lipid accumulation and inflammatory processes in arterial walls, with oxidized low-density lipoprotein (OxLDL) playing a central role. OxLDL is increased during aging and stimulates monocyte transformation into foam cells and induces metabolic reprogramming and pro-inflammatory responses, accelerating atherosclerosis progression and contributing to other age-related diseases. This study investigated the effects of Mdivi-1, a mitochondrial fission inhibitor, and S1QEL, a selective complex I-associated reactive oxygen species (ROS) inhibitor, on OxLDL-induced responses in monocytes.
View Article and Find Full Text PDFRSC Adv
January 2025
Department of Life Science and Applied Chemistry, Graduate School of Engineering, Nagoya Institute of Technology Gokiso-cho, Showa-ku Nagoya Aichi Japan 466-8555
We recently proposed a concept of self-transformation from thermoplastic polyesters into vitrimers intermolecular bond exchange as the cross-linking reaction. Key was the use of polyesters bearing hydroxyl side groups, which were cross-linked without additional cross-linkers through intermolecular transesterification in the presence of a suitable catalyst. In our previous study, a linear polyester was synthesized as the starting polymer by reacting dithiol monomers containing ester bonds (2-SH) with diepoxy monomers (2-epoxy) a thiol-epoxy reaction, generating hydroxyl side groups along the polyester chain.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Microbiology Dr D. Y. Patil Medical College, Hospital and Research Centre, Dr D. Y. Patil Vidyapeeth (Deemed-to-be-University) Pune Maharashtra India.
Background And Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.
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