Browsing by Author "Aslan, Arda"

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Citation - WoS: 2
Citation - Scopus: 2
Liraglutide Modulates Cyclooxygenase and α7 Acetylcholine Receptors: in Vitro and in Silico Insights Into Its Anti-Inflammatory Role in LPS-Induced Inflammation in Raw 264.7 Macrophages
(Springer, 2025) Baris, Elif; Portakal, Huseyin Saygin; Aslan, Arda; Karagonlar, Zeynep Firtina; Tosun, Metiner; Firtina Karagonlar, Zeynep
Liraglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, is well-established for its metabolic benefits, including glycemic control and weight loss. Beyond these roles, it exhibits significant anti-inflammatory properties, though the mechanisms remain underexplored. This study investigates the anti-inflammatory effects of liraglutide in lipopolysaccharide (LPS)-stimulated RAW 264.7 murine macrophages. Results demonstrate that increasing concentrations of liraglutide suppresses LPS-elevated prostaglandin E2 (PGE2), 6-keto prostaglandin F1 alpha (6-keto-PGF1 alpha, a stable prostacyclin metabolite) and thromboxane A2 (TXA2), similar to that observed with conventional anti-inflammatory agents, ibuprofen and celecoxib. Mechanistic exploration reveals that liraglutide's anti-inflammatory action is dually-modulated by cyclooxygenase (COX) and nicotinic acetylcholine receptor (nAChR) signaling. The application of non-selective, non-competitive nAChR antagonist or selective and potent alpha 7-nAChR antagonist, mecamylamine (MEC) and methyllycaconitine (MLA), respectively, highlights the involvement of cholinergic pathways in liraglutide's activity. Based on in silico molecular docking analyses, liraglutide exhibits favorable binding affinities to COX-1, COX-2, prostacyclin synthase (PGIS), and alpha 7nAChRs, supporting its potential multi-target anti-inflammatory effects. These findings suggest that the therapeutic potential of liraglutide may go beyond metabolic regulation and may be promising for conditions in which metabolic and inflammatory pathways converge, including inflammation and modulation of cholinergic signaling.
Viability Analysis of Drug-Treated Tumor Spheroids Using Machine Learning
(IEEE, 2024) Oguz, Kaya; Aslan, Arda; Evcin, Emre; Ozogul, Emre; Sonmez, Mehmet Eren; Karabacak, Yaren; Karagonlar, Zeynep Firtina
3D spheroids that are able to mimic the microenvironment of tumors effectively have emerged as significant structures in cancer biology and drug development. This study aims to help cancer researchers monitor the changes in human liver cancer spheroids in response to drug treatment by offering a software tool for evaluating cell viability within 3D spheroids. A dataset of spheroid images are collected, processed, and classified using alternative machine learning models constructed with Random Forest, Logistic Regression, Support Vector Machine and Extreme Gradient Boosting methods. The classification performances of the models are evaluated in terms of the prediction accuracy, precision, recall, and F1-score values. Based on the test experiments conducted, Extreme Gradient Boosting model achieved the highest ratios for all of the performance metrics. Furthermore, a standalone desktop application is implemented to perform analyses of the images with the help of its user-friendly interface.