PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - Scopus: 23Synthesis and Comprehensive in Vivo Activity Profiling of Olean-12-en-28-ol, 3β-Pentacosanoate in Experimental Autoimmune Encephalomyelitis: A Natural Remyelinating and Anti-Inflammatory Agent(American Chemical Society, 2023-01-04) Şenol, Halil; Ozgun-Acar, Özden; Daǧ, Aydan; Eken, Ahmet; Guner, Hüseyin; Aykut, Zaliha Gamze; Sen, AlaattinMultiple sclerosis (MS) treatment has received much attention, yet there is still no certain cure. We herein investigate the therapeutic effect of olean-12-en-28-ol, 3β-pentacosanoate (OPCA) on a preclinical model of MS. First, OPCA was synthesized semisynthetically and characterized. Then, the mice with MOG<inf>35-55</inf>-induced experimental autoimmune/allergic encephalomyelitis (EAE) were given OPCA along with a reference drug (FTY720). Biochemical, cellular, and molecular analyses were performed in serum and brain tissues to measure anti-inflammatory and neuroprotective responses. OPCA treatment protected EAE-induced changes in mouse brains maintaining blood-brain barrier integrity and preventing inflammation. Moreover, the protein and mRNA levels of MS-related genes such as HLD-DR1, CCL5, TNF-α, IL6, and TGFB1 were significantly reduced in OPCA-treated mouse brains. Notably, the expression of genes, including PLP, MBP, and MAG, involved in the development and structure of myelin was significantly elevated in OPCA-treated EAE. Furthermore, therapeutic OPCA effects included a substantial reduction in pro-inflammatory cytokines in the serum of treated EAE animals. Lastly, following OPCA treatment, the promoter regions for most inflammatory regulators were hypermethylated. These data support that OPCA is a valuable and appealing candidate for human MS treatment since OPCA not only normalizes the pro- and anti-inflammatory immunological bias but also stimulates remyelination in EAE. © 2023 Elsevier B.V., All rights reserved.Article Citation - Scopus: 25Recursive Cluster Elimination Based Rank Function (SVM-RCE-R) Implemented in KNIME(F1000 Research Ltd, 2021-01-05) Yousef, Malik; Bakir-Güngör, Burcu; Jabeer, Amhar; Göy, Gökhan; Qureshi, Rehman A.; C Showe, Louise; C. Showe, LouiseIn our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify MicroRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. © 2021 Elsevier B.V., All rights reserved.Article Citation - Scopus: 302Molecular Mechanisms of Drug Resistance and Its Reversal in Cancer(Taylor and Francis Ltd healthcare.enquiries@informa.com, 2015-03-11) Kartal Yandim, Melis; Adan Gökbulut, Aysun; Baran, Yusuf; Adan-Gokbulut, Aysun; Kartal-Yandim, MelisChemotherapy is the main strategy for the treatment of cancer. However, the main problem limiting the success of chemotherapy is the development of multidrug resistance. The resistance can be intrinsic or acquired. The resistance phenotype is associated with the tumor cells that gain a cross-resistance to a large range of drugs that are structurally and functionally different. Multidrug resistance arises via many unrelated mechanisms, such as overexpression of energy-dependent efflux proteins, decrease in uptake of the agents, increase or alteration in drug targets, modification of cell cycle checkpoints, inactivation of the agents, compartmentalization of the agents, inhibition of apoptosis and aberrant bioactive sphingolipid metabolism. Exact elucidation of resistance mechanisms and molecular and biochemical approaches to overcome multidrug resistance have been a major goal in cancer research. This review comprises the mechanisms guiding multidrug resistance in cancer chemotherapy and also touches on approaches for reversing the resistance. © 2017 Elsevier B.V., All rights reserved.Article Citation - WoS: 4Citation - Scopus: 4Integrated Querying and Version Control of Context-Specific Biological Networks(Oxford Univ Press, 2020) Cowman, Tyler; Coskun, Mustafa; Grama, Ananth; Koyuturk, MehmetMotivation: Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. Results: We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. Conclusion: Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. Availability and Implementation: VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion Contact: tyler.cowman@case.eduArticle Citation - Scopus: 9Cerium Oxide Nanoparticles Biosynthesized Using Fresh Green Walnut Shell in Microwave Environment and Their Anticancer Effect on Breast Cancer Cells(John Wiley and Sons Inc, 2022-07-12) Sulak, Mine; Turgut, Gurbet Çelik; Sen, AlaattinIn this study, cerium oxide nanoparticles (CONPs) were synthesized using fresh green walnut shell extract in microwave environment. The morphology and structure of the CONPs were determined using ultraviolet-visible (UV/VIS), attenuated total reflection-Fourier transform infrared (ATR-FT-IR), X-ray diffraction (XRD), energy-dispersive X-ray (EDX) spectroscopy, and scanning electron microscopy (SEM). Crystal purple staining, Annexin V-FITC detection, RT-PCR, P53, and NF-κB luciferase reporter assays were performed to evaluate the mechanism of action of CONPs in breast cancer cell lines (MCF7). The biosynthesized CONPs showed cytotoxic effects and induced apoptosis in MCF7 cells. Furthermore, CONPs induced P53 expression and suppressed NF-κB gene expression, both of which were confirmed using reporter assays. Based on the present results, it was concluded that CONPs can induce apoptosis by acting on P53 at the transcriptional level and may cause cell death by suppressing NF-κB-mediated transcription. © 2022 Elsevier B.V., All rights reserved.Article Citation - Scopus: 4CCPred: Global and Population-Specific Colorectal Cancer Prediction and Metagenomic Biomarker Identification at Different Molecular Levels Using Machine Learning Techniques(Elsevier Ltd, 2024-11) Bakir-Güngör, Burcu; Temiz, Mustafa; Inal, Yasin; Cicekyurt, Emre; Yousef, MalikColorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2′ 3′ cyclic 3′ phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 8Building a Challenging Medical Dataset for Comparative Evaluation of Classifier Capabilities(Elsevier Ltd, 2024-08) Bozkurt, Berat; Coskun, Kerem; Bakal, GokhanSince the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual data to be processed, including articles, clinical records, web pages, and shared social media posts. As a critical analysis, the classification task classifies the given textual entities into correct categories. Categorizing documents from different domains is straightforward since the instances are unlikely to contain similar contexts. However, document classification in a single domain is more complicated due to sharing the same context. Thus, we aim to classify medical articles about four common cancer types (Leukemia, Non-Hodgkin Lymphoma, Bladder Cancer, and Thyroid Cancer) by constructing machine learning and deep learning models. We used 383,914 medical articles about four common cancer types collected by the PubMed API. To build classification models, we split the dataset into 70% as training, 20% as testing, and 10% as validation. We built widely used machine-learning (Logistic Regression, XGBoost, CatBoost, and Random Forest Classifiers) and modern deep-learning (convolutional neural networks - CNN, long short-term memory - LSTM, and gated recurrent unit - GRU) models. We computed the average classification performances (precision, recall, F-score) to evaluate the models over ten distinct dataset splits. The best-performing deep learning model(s) yielded a superior F1 score of 98%. However, traditional machine learning models also achieved reasonably high F1 scores, 95% for the worst-performing case. Ultimately, we constructed multiple models to classify articles, which compose a hard-to-classify dataset in the medical domain. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 4Barriers to Strategy Implementation in Turkey’s Healthcare Industry: Hospital Manager Perspectives(Informa UK Ltd, 2021-08-02) Ocak, Saffet; Aladag, Omer Faruk; Köseoglu, Mehmet Ali; King, Brian E.M.Although strategy implementation has profound implications for delivering efficient service, it has been largely neglected in the healthcare management literature. This study explores the barriers to effective implementation of strategic plans in healthcare organizations. To achieve this end, empirical data were collected from 185 hospital managers in Turkey using a survey-based methodology. A descriptive analysis was undertaken of the survey responses to determine the most important barriers to strategy implementation. The most significant barriers undermining strategy implementation efforts were found to be: low employee motivation, an exclusive focus on financial performance and lack of consensus among decision makers. © 2023 Elsevier B.V., All rights reserved.Article Citation - Scopus: 15An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning(Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentEarly detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.