Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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  • Review
    Citation - WoS: 1
    Citation - Scopus: 2
    Organ-On Platforms for Drug Development, Cellular Toxicity Assessment, and Disease Modeling
    (Tubitak Scientific & Technological Research Council Turkey, 2024) Khurram, Muhammad Maaz; Bedir, Erdal; Cinel, Gokturk; Yesil Celiktas, Ozlem; Bedir, Erdal; 03.01. Department of Bioengineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Organs-on-chips (OoCs) or microphysiological platforms are biomimetic systems engineered to emulate organ structures on microfluidic devices for biomedical research. These microdevices can mimic biological environments that enable cell-cell interactions on a small scale by mimicking 3D in vivo microenvironments outside the body. Thus far, numerous single and multiple OoCs that mimic organs have been developed, and they have emerged as forerunners for drug efficacy and cytotoxicity testing. This review explores OoC platforms to highlight their versatility in studies of drug safety, efficacy, and toxicity. We also reflect on the potential of OoCs to effectively portray disease models for possible novel therapeutics, which is difficult to achieve with traditional 2D in vitro models, providing an essential basis for biologically relevant research.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Β-Ketoenamine-linked covalent organic framework for efficient iodine capture
    (Tubitak Scientific & Technological Research Council Turkey, 2024) Büyükçakır, Onur; Büyükçakır, Onur; 04.01. Department of Chemistry; 04. Faculty of Science; 01. Izmir Institute of Technology
    Exploring the materials that effectively capture radioactive iodine is crucial in managing nuclear waste produced from nuclear power plants. In this study, a β-ketoenamine-linked covalent organic framework (bCOF) is reported as an effective adsorbent to capture iodine from both vapor and solution. The bCOF’s high porosity and heteroatom-rich skeleton offer notable iodine vapor uptake capacity of up to 2.51g $g^{–1}$ at 75 °C under ambient pressure. Furthermore, after five consecutive adsorption-desorption cycles, the bCOF demonstrates high reusability performance with significant iodine vapor capacity retention. The adsorption mechanism was also investigated using various ex situ structural characterization techniques, and these mechanistic studies revealed the existence of a strong chemical interaction between the bCOF and iodine. The bCOF also showed good iodine uptake performance of up to 512 mg $g^{–1}$ in cyclohexane with high removal efficiencies. The bCOF’s performance in adsorbing iodine from both vapor and solution makes it a promising material to be used as an effective adsorbent in capturing radioactive iodine emissions from nuclear power plants.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Enrichment of Turkish Question Answering Systems Using Knowledge Graphs
    (Tubitak Scientific & Technological Research Council Turkey, 2024) Tekir, Selma; Soygazi, Fatih; Tekir, Selma; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering dataset (TRMQA) to answer questions in the movie domain. We evaluate our proposed question answering pipeline against a baseline study. Furthermore, we compare it with a question answering system built upon GPT-3.5 Turbo to answer the 1-hop questions from TRMQA. The experimental results confirm that link prediction on a knowledge graph is quite effective in answering questions that require reasoning paths. Finally, we provide insights into the pros and cons of the provided solution through a qualitative study.