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, and as Potential Functional Food Ingredients for the Management of Metabolic Syndrome.
Foods
January 2025
Food BioSciences, Teagasc Food Research Centre, Dunsinea Lane, Ashtown, D15 DY05 Dublin, Ireland.
Hypertension, type 2 diabetes (T2D), and obesity raise an individual's risk of suffering from diseases associated with metabolic syndrome (MS). In humans, enzymes that play a role in the prevention and development of MS include angiotensin converting enzyme (ACE-1) associated with hypertension, α-amylase associated with T2D, and lipase linked to the development of obesity. Seaweeds are a rich source of bioactives consisting of proteins/peptides, polysaccharides, and lipids.
View Article and Find Full Text PDFCurcumin enhances the anti-obesogenic activity of orlistat through SKN-1/NRF2-dependent regulation of nutrient metabolism in Caenorhabditis elegans.
Int J Obes (Lond)
January 2025
Laboratory of Metabolomics, Department of Biotechnology, Institute of Microbiology, Bulgarian Academy of Sciences, 139 Ruski Blvd., 4000, Plovdiv, Bulgaria.
Background: Metabolic dysregulation, a defining feature of obesity, disrupts essential signalling pathways involved in nutrient sensing and mitochondria homeostasis. The nuclear factor erythroid 2-related factor 2 (NRF-2) serves as a pivotal regulator of the cellular stress response, and recent studies have implicated it in the pathogenesis of obesity, diabetes, and metabolic syndrome. Curcumin, a polyphenolic compound derived from turmeric, has been identified as a potent activator of NRF-2.
View Article and Find Full Text PDFEfficacy of Oral Medication in Weight Loss Management: A Systematic Review and Network Meta-Analysis.
Clin Ther
January 2025
Department of Pharmacology, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Indonesia. Electronic address:
Purpose: This systematic review was conducted to determine which type of oral medication for obesity provides the best weight loss effect.
Methods: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guideline. For this systematic review, we used 3 databases for journal searches: PubMed, ScienceDirect, and Scopus.
HACD2 Promotes Pancreatic Cancer Progression by Enhancing PKM2 Dissociation From PRKN in a Dehydratase-Independent Manner.
Adv Sci (Weinh)
January 2025
Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China.
3-Hydroxyacyl-CoA dehydratase 2 (HACD2), an obesity-related gene involved in the elongation of long-chain fatty acids, is highly expressed in pancreatic cancer (PC) and is associated with patient prognosis. Interestingly, the study reveals that HACD2 mediated the proliferation of PC cells in a dehydratase-independent manner, affecting the downstream glycolytic pathway. Mechanistically, HACD2 promotes PC cells proliferation by binding to E3 ubiquitin-protein ligase parkin (PRKN) and enhancing pyruvate kinase PKM (PKM2) dissociation from PRKN, resulting in reduced ubiquitination of PKM2 and increased dimerization of PKM2, which subsequently promote c-Myc expression and tumor growth.
View Article and Find Full Text PDFIntegrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.
Sci Rep
January 2025
State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds.
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