Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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: 25Synthesis 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: 1Robust Controller Electromyogram Prosthetic Hand With Artificial Neural Network Control and Position(Indian Journal of Forensic Medicine and Toxicology ijfmt@hotmail.com, 2020) Ahmed, Saygin Siddiq; Ahmed, Aydin S.; Yilmaz, Bulent; Doǧru, NuranIn this study, we proposed and designed a new control method for an electromyographically (EMG) controlled prosthetic hand. The objective is to increase the control efficiency of the human–machine interface and afford greater control of the prosthetic hand. The process works as follows: EMG biomedical signals acquired from Myoware sensors positioned on the relevant muscles are sent to the robot that consist of hand, Arduino and MATLAB program, which computes and controls the hand position in free space along with hand grasping operations. The Myoware device acquires muscle signals and sends them to the Arduino. The Arduino analyzes the received signals, based on which it controls the motor movement. In this design, the muscle signals are read and saved in a MATLAB system file. After program processing on the industrial hand which is applied by MATLAB simulation, the corresponding movement is transferred to the hand, enabling movements, such as, hand opening and closing according to the signal stored in the MATLAB system. In this study, hand and fingerprints were designed using a three-dimensional printer by separate recording finger and thumb signals. The muscle signals were then analyzed in order to obtain peak signal points and convert them into data. These results indicate the effectiveness of the proposed method and demonstrate the superiority of the method for amputees because of the improved controllability and perceptibility afforded by the design. © 2020 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: 2Improving Short-Term Memory Performance of Healthy Young Males Using Alpha Band Neurofeedback(International Society for Neurofeedback and Research, 2019-03-24) Gökşin, Barış; Yilmaz, Bulent; İçöz, KutayTo examine whether it was possible to improve short-term memory performance of healthy participants by increasing relative alpha band power (7–11.5 Hz) using neurofeedback, we first converted a commercial EEG device (EmotivEpoc) to a neurofeedback tool and collected data from 11 healthy Turkish male graduate students in five neurofeedback sessions. Before and after neurofeedback training, a memorization task using 10 English words and their Turkish meanings was applied to all participants. The results indicated that 6 out of 11 participants were able to enhance their relative alpha band power with respect to other bands in the frequency spectrum during neurofeedback sessions. Although there was no obvious improvement in their short-term memory performance, we may conclude that neurofeedback training was beneficial for the participants to focus their minds consciously. However, it is not easy to mention that neurofeedback training certainly improved or was irrelevant with short-term memory performance. This study is important in the sense that for such a focused group the use of a commercial, customized low-cost EEG device was shown to be feasible for neurofeedback training sessions. © 2019 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: 5CCPred: 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: 10Building 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.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.