Background: Diabetes mellitus is a serious global health issue, currently affecting 425 million people and is set to affect over 690 million people by 2045. It is a chronic disease characterized by hyperglycemia due to relative or absolute insulin hormone deficiency. Dipeptidyl peptidase- IV (DPP-IV) inhibitors are hypoglycemic agents augmenting the action of the incretin hormones that stimulate insulin secretion from the pancreatic beta cells.
Objective: In this study, synthesis and biological evaluation of seven piperazine derivatives 3a-g was carried out.
Methods: The synthesized molecules were characterized using proton-nuclear magnetic resonance, carbon-nuclear magnetic resonance, infrared spectroscopy and mass spectrometry.
Results: In vitro biological evaluation study showed comparable DPP-IV inhibitory activity for the targeted compounds ranging from 19%-30% at 100 μM concentration. Furthermore, the in vivo hypoglycemic activity of 3d was evaluated using streptozotocin-induced diabetic mice. It was found that compound 3d significantly decreased the blood glucose level when the diabetic group treated with 3d was compared to the control diabetic group. Quantum-Polarized Ligand Docking (QPLD) studies demonstrate that 3a-g fit the binding site of DPP-IV enzyme and form H-bonding with the backbones of R125, E205, E206, K554, W629, Y631, Y662, R669, and Y752.
Conclusion: Piperazine derivatives were successfully found to be new scaffolds as potential DPP-IV inhibitors.
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http://dx.doi.org/10.2174/1573406416666200917105401 | DOI Listing |
BMC Pharmacol Toxicol
January 2025
Yanzhou District People's Hospital, Jining, Shandong, China.
Background: Osteoporosis (OP), often termed the "silent epidemic," poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across various diseases.
Methods: This study explored FBXW4 by integrating DEGs from GEO datasets GSE2208, GSE7158, GSE56815, and GSE35956 with immune-related gene compilations from the ImmPort repository.
Cancer Cell Int
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Methods: Multiple public datasets and an in-house cohort were utilized to assess the clinical relevance of DAAM1 in pancreatic cancer.
Crit Care
January 2025
Department of Anesthesiology, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave, Wuhan, 430030, China.
Background: Ulinastatin (UTI), recognized for its anti-inflammatory properties, holds promise for patients undergoing cardiac surgery. This study aimed to investigate the relationship between intraoperative UTI administration and the incidence of delirium following cardiac surgery.
Methods: A retrospective analysis was performed on a retrospective cohort of 6,522 adult cardiac surgery patients to evaluate the relationship between UTI treatment and the incident of postoperative delirium (POD) in patients ongoing cardiac surgery.
BMC Genom Data
January 2025
School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Office 101E, Ottawa, Ontario, K1G 5Z3, Canada.
High intraocular pressure (IOP) is an important risk factor for glaucoma, which is influenced by genetic and environmental factors. However, the etiology of high IOP remains uncertain. Metabolites are compounds involved in metabolism which provide a link between the internal (genetic) and external environments.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.
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