Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    K41-A Enhances the Antiproliferative Efficacy of Cisplatin in Neuroblastoma by Modulating Apoptosis and Autophagy
    (Oxford University Press, 2026) Sanlav, Gamze; Kum Ozsengezer, Selen; Altun, Zekiye; Bedir, Erdal; Aktas, Safiye; Olgun, Nur
    Objectives Neuroblastoma (NB), the most common extracranial tumor in childhood, has a poor prognosis, especially in cases with MYC gene amplification. Cisplatin (CDDP) is widely used in treatment, but its effectiveness is limited due to chemotherapy resistance. Autophagy plays a dual role in cancer progression, either promoting survival or contributing to cell death.Methods This study explores the anticancer effects of K41-A, a polycyclic polyether molecule, alone and in combination with CDDP in SH-SY5Y and KELLY NB cell lines, the HE-IOC1 noncancerous cochlear cell line, and the NB xenograft model.Key findings For the first time, we demonstrate that K41-A, either alone or combined with CDDP, significantly inhibits cell proliferation selectively in NB cells, sparing noncancerous cells. This study confirmed that K41-A alone and in combination with CDDP induced changes in both apoptotic and autophagic cell death components in NB, resulting in antiproliferative activity in vitro and in vivo. In addition, the combination with CDDP enhanced the therapeutic efficacy of K41-A.Conclusions These results highlight the potential of K41-A as a candidate drug for the treatment of NB.
  • Article
    Citation - WoS: 13
    Citation - Scopus: 18
    Graph Matching-Based Distributed Clustering and Backbone Formation Algorithms for Sensor Networks
    (Oxford University Press, 2010) Dağdeviren, Orhan; Erciyeş, Kayhan
    Clustering is a widely used technique to manage the essential operations such as routing and data aggregation in wireless sensor networks (WSNs). We propose two new graph-theoretic distributed clustering algorithms for WSNs that use a weighted matching method for selecting strong links. To the best of our knowledge, our algorithms are the first attempts that use graph matching for clustering. The first algorithm is divided into rounds; extended weighted matching operation is executed by nodes in each round; thus the clusters are constructed synchronously. The second algorithm is the enhanced version of the first algorithm, which provides not only clustering but also backbone formation in an energy-efficient and asynchronous manner. We show the operation of the algorithms, analyze them, provide the simulation results in an ns2 environment. We compare our proposed algorithms with the other graph-theoretic clustering algorithms and show that our algorithms select strong communication links and create a controllable number of balanced clusters while providing low-energy consumptions. We also discuss possible applications that may use the structure provided by these algorithms and the extensions to the algorithms. © The Author 2009. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.