PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - Scopus: 30Thermochemistry of Alkali Metal Cation Interactions With Histidine: Influence of the Side Chain(2012-11-26) Armentrout, Peter B.; Citir, Murat; Chen, Yu; Rodgers, Mary T.The interactions of alkali metal cations (M+ = Na+, K+, Rb+, Cs+) with the amino acid histidine (His) are examined in detail. Experimentally, bond energies are determined using threshold collision-induced dissociation of the M+(His) complexes with xenon in a guided ion beam tandem mass spectrometer. Analyses of the energy dependent cross sections provide 0 K bond energies of 2.31 ± 0.11, 1.70 ± 0.08, 1.42 ± 0.06, and 1.22 ± 0.06 eV for complexes of His with Na+, K+, Rb+, and Cs+, respectively. All bond dissociation energy (BDE) determinations include consideration of unimolecular decay rates, internal energy of reactant ions, and multiple ion-neutral collisions. These experimental results are compared to values obtained from quantum chemical calculations conducted previously at the MP2(full)/6-311+G(2d,2p), B3LYP/6-311+G(2d,2p), and B3P86/6-311+G(2d,2p) levels with geometries and zero point energies calculated at the B3LYP/6-311+G(d,p) level where Rb and Cs use the Hay-Wadt effective core potential and basis set augmented with additional polarization functions (HW*). Additional calculations using the def2-TZVPPD basis set with B3LYP geometries were conducted here at all three levels of theory. Either basis set yields similar results for Na+(His) and K+(His), which are in reasonable agreement with the experimental BDEs. For Rb+(His) and Cs +(His), the HW* basis set and ECP underestimate the experimental BDEs, whereas the def2-TZVPPD basis set yields results in good agreement. The effect of the imidazole side chain on the BDEs is examined by comparing the present results with previous thermochemistry for other amino acids. Both polarizability and the local dipole moment of the side chain are influential in the energetics. © 2012 American Chemical Society. © 2013 Elsevier B.V., All rights reserved.; MEDLINE® is the source for the MeSH terms of this document.Article Citation - Scopus: 2Prediction of Colorectal Cancer Based on Taxonomic Levels of Microorganisms and Discovery of Taxonomic Biomarkers Using the Grouping-Scoring (G-S-M) Approach(Elsevier Ltd, 2025-03) Bakir-Güngör, Burcu; Temiz, Mustafa; Canakcimaksutoglu, Beyza; Yousef, MalikColorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1Possible Drug-Drug Interactions Between Mesalamine and Tricyclic Antidepressants Through CYP2D6 Metabolism - in Silico and in Vitro Analyses(Georg Thieme Verlag, 2025-04-01) Ozen, Melek B.; Gazioğlu, Işil; Ozgun-Acar, Özden; Guner, Hüseyin; Semiz, Gürkan; Sen, Alaattin; Ozgun Acar, OzdenMesalamine (mesalazine, 5-aminosalicylic acid, 5-ASA) is an essential anti-inflammatory agent both used for therapy and as a remission control in patients with inflammatory bowel diseases (IBD) such as ulcerative colitis (UC). Tricyclic antidepressants (TCAs) are used to alleviate remaining symptoms in patients already receiving IBD therapy or with quiescent inflammation. The cytochrome P4502D6 enzyme is involved in the metabolism of TCAs. Hence, it is crucial to investigate the role of CYP2D6 in 5-ASA metabolism. Initially, in silico analysis involving the docking of 5-ASA to CYP2D6 and molecular dynamics simulations was conducted. Next, the rate of O-demethylation of a nonfluorescent probe 3-[2-(N,N-diethyl-N-methylammonium)-ethyl]-7-methoxy-4-methylcoumarin (AMMC) into a fluorescent metabolite AMHC (3-[2-(N,N-diethyl-N-methylammonium)ethyl]-7-hydroxy-4-methylcoumarin) was optimized with baculosomes co-expressing human CYP2D6 and human P450 oxidoreductase (hCPR) to monitor CYP2D6 activity in a microtiter plate assay. The apparent Km and Vmax were found to be 1.30 μM and 32.68 pmol/min/mg of protein for the O-demethylation of AMMC to AMHC, and the reaction was linear for 40 min. Then, nonselective inhibition of CYP2D6 activity with various concentrations of 5-ASA was detected. Finally, the conversion of AMMC to metabolites was analyzed by HPLC-ESI-MS/MS spectrometry, and none were identified. Thus, this study suggests that concurrent use of mesalamine with TCA may lead to adverse effects, and CYP2D6 genotyping should be routinely performed on these patients to eliminate possible threats. © 2025 Elsevier B.V., All rights reserved.Article Evaluation of HOTAIR, HOXD8, HOXD9, HOXD11 Gene Expression Levels in Turkish Patients With Acute and Chronic Myeloid Leukemia: A Single Center Experience(Cellular and Molecular Biology Association, 2024-11-27) Saraymen, Esma; Erdem, Yakut; Akalin, Hilal Ünlü; Taşçıoğlu, Nazife; Saraymen, Berkay; Celik, Serhat; Özkul, Yusuf T.Homeobox (HOX) transcript antisense RNA (HOTAIR) and HOX genes are reported to be more expressed in various cancers in humans in recent studies. The role of HOTAIR and HOXD genes in acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) is not well known. In this study, expression levels of HOXD8, HOXD9 and HOXD11 from HOXD gene family and HOTAIR were determined from peripheral blood samples of 30 AML and 30 CML patients and 20 healthy volunteers by quantitative Real Time PCR. We determined that the expression levels of HOXD9 and HOXD11 in the AML patients were significantly lower than the control group (p<0.001 and p=0.002, respectively). There was no significant difference in the expression levels of HOTAIR and HOXD8 when compared to the control group. In the CML patients there was a significant increase in the expression level of HOTAIR when compared to the control group (p=0.002). The expression levels of HOXD9 and HOXD11 were found to be significantly lower than the control group (p<0.001). Our study showed that HOTAIR may not be a biomarker in the diagnosis and is not significantly correlated with the clinicopathological prognostic characteristics of AML. Additionally; it can be said that HOTAIR is oncogenic by suppressing the expression of HOXD9 and HOXD11 but not HOXD8 in CML patients. The expression profiles of HOTAIR may be a potential biomarker in the diagnosis of CML patients in predicting and monitoring drug resistance. © 2025 Elsevier B.V., All rights reserved.Article 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 - WoS: 3Citation - Scopus: 3An FDTD-Based Computer Simulation Platform for Shock Wave Propagation in Electrohydraulic Lithotripsy(Elsevier Ireland Ltd, 2013-06) Yilmaz, Bulent; Çiftçi, EmreExtracorporeal Shock Wave Lithotripsy (ESWL) is based on disintegration of the kidney stone by delivering high-energy shock waves that are created outside the body and transmitted through the skin and body tissues. Nowadays high-energy shock waves are also used in orthopedic operations and investigated to be used in the treatment of myocardial infarction and cancer. Because of these new application areas novel lithotriptor designs are needed for different kinds of treatment strategies. In this study our aim was to develop a versatile computer simulation environment which would give the device designers working on various medical applications that use shock wave principle a substantial amount of flexibility while testing the effects of new parameters such as reflector size, material properties of the medium, water temperature, and different clinical scenarios. For this purpose, we created a finite-difference time-domain (FDTD)-based computational model in which most of the physical system parameters were defined as an input and/or as a variable in the simulations. We constructed a realistic computational model of a commercial electrohydraulic lithotriptor and optimized our simulation program using the results that were obtained by the manufacturer in an experimental setup. We, then, compared the simulation results with the results from an experimental setup in which oxygen level in water was varied. Finally, we studied the effects of changing the input parameters like ellipsoid size and material, temperature change in the wave propagation media, and shock wave source point misalignment. The simulation results were consistent with the experimental results and expected effects of variation in physical parameters of the system. The results of this study encourage further investigation and provide adequate evidence that the numerical modeling of a shock wave therapy system is feasible and can provide a practical means to test novel ideas in new device design procedures. © 2012 Elsevier Ireland Ltd. © 2014 Elsevier B.V., All rights reserved.; MEDLINE® is the source for the MeSH terms of this document.