PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Performance Boost in QLEDs Using Octanethiol-Capped Core/Shell Quantum Dots(IOP Publishing Ltd, 2026-01-07) Yazici, Ahmet F.; Yuruc, Adnan M.; Kelestemur, Yusuf; Serin, Ramis Berkay; Kacar, Rifat; Ulku, Alper; Mutlugun, EvrenQuantum dots attract significant attention as one of the most promising colloidal nanocrystals with unique optical properties and potential applications for the next generation of display technology. In this paper, we evaluate the performance of CdZnSeS-based alloyed-shell quantum dots (QDs) for electroluminescence devices upon additional shell growth and ligand exchange. This includes core/shell (C/S) and core/shell/shell (C/S/S) QDs, whose latter includes an additional ZnS shell and octanethiol (OT) ligands. We present detailed characterizations of QDs using transmission electron microscopy, XRD, and various spectroscopic techniques and demonstrate their QD light emitting (QLEDs). We find the photoluminescence quantum yield of C/S/S QDs increased from 68.8% to 88.7% compared to C/S QDs whereas the emission linewidth narrows from 22.2 nm to 20.8 nm. QLEDs fabricated with C/S/S QDs exhibit a higher peak external quantum efficiency (EQE) of 4.1% and maximum luminance of 85 000 cd m-2, compared to 2.3% EQE and 67 000 cd m-2 for C/S QLEDs. In this respect, the OT-assisted shell growth significantly improves the optical property of QDs and performance of QLEDs, likely attributed to the enhanced charge balance and increased radiative recombination rate.Article A Small Indole Derivative Isolated From Caper (Capparis Ovata) as an Inducer of P53-Mediated Apoptosis in Prostate Cancer: Comprehensive In Vitro and In Silico Studies(Wiley, 2025-12-31) Acar, Ozden Ozgun; Gazioglu, Isil; Oruc, Hatice; Kale, Elif; Senol, Halil; Topcu, Gulacti; Sen, AlaattinNatural products with stunning chemical diversity have been extensively researched for their anticancer potential for more than fifty years. This study aimed to determine the effect of indole derivative 1H-indole-2-hydroxy-3-carboxylic acid (IHCA), isolated as a novel alkaloid from Capparis ovata, on selected tumor suppressor, apoptotic, and cell cycle regulatory genes, which are known to be important in cancer pathophysiology, on Caco-2 and LNCaP cells in comparison with Taxol. The molecular mechanism of IHCA's anticancer activity is essentially undefined. Different concentrations of IHCA increased the expression levels of apoptosis-related genes, including BCL-2 and TNF-alpha. In addition, the tumor suppressor genes PTEN, P53, and RB were increased in LNCaP and Caco-2 cells. KRAS, an oncogenic gene, was significantly downregulated by IHCA in LNCaP cells. Western blot results showed that the protein expression levels of P53 and PTEN in LNCaP cells were increased when treated with IHCA, whereas CDK4 and TNF-alpha were decreased. Finally, IHCA and doxorubicin significantly increased P53-driven luciferase activity compared to the control. The results strongly suggest that the novel natural compound IHCA has an anticancer effect involving the regulation of the P53 gene and its networks in vitro. The molecular docking and MD simulation analyses reveal that IHCA exhibits superior binding potential to the MDM2 protein compared to Nutlin-3a. MD simulations further confirm that IHCA maintains a more stable and consistent interaction with MDM2, as indicated by lower RMSD values and reduced ligand fluctuation. These results highlight IHCA's potential as a more effective MDM2 inhibitor, suggesting its promise as a lead compound for anticancer drug development.Clinical Trial Registration: Not applicable.Editorial Advances in Natural Building and Construction Materials(MDPI, 2025-12-16) Strzalkowski, Pawel; Sousa, Luis; Koken, Ekin; Strzałkowski, PawełArticle A Potential Hemostatic Chitosan/Gelatin Cryogel Impregnated with Verbascum Thapsus Leaf Extract for Noncompressible Hemorrhage Management(IOP Publishing Ltd, 2025-11-01) Uzuner, Hacernur; Yuruk, Adile; Isoglu, Ismail AlperIn this study, we prepared a series of chitosan/gelatin (CS/GEL) cryogels containing Verbascum thapsus (V. thapsus) leaf extract and identified a lead formulation for noncompressible hemorrhage (NCH). Cryogels with average pore diameters ranging from 225 to 478 mu m were fabricated through cryogelation at various CS/GEL ratios. C15 was chosen as the base scaffold due to its homogeneous pore distribution, with a pore size coefficient of variation (CV) of approximately 0.22. Extract loading was 1%, 5%, 10%, and 20% w/v. Functional porosity was reported by the relative accessible void index (RAVI). In PBS, the values relative to neat C15 were 1.00, 0.27, 0.20, 0.13, and 0.09 for concentrations of 0%, 1%, 5%, 10%, and 20% w/v, respectively. In citrated blood, the series was 1.00, 0.29, 0.12, 0.14, and 0.09. After loading, equilibrium swelling decreased and the compressive modulus increased, consistent with partial pore filling in a fixed network. The cryogels maintained an interconnected macroporous network and showed swelling from 300% to 3600% in blood and PBS. Antibacterial activity reached 89% inhibition, and cell viability remained above 80%. Hemolysis was low and within acceptance limits. Clotting improved in whole blood as the blood clotting index decreased from 11.9 to 6.5, and the clotting time was approximately 6 min. The 5% w/v group provided the optimal balance of clotting, antibacterial effects, and biocompatibility. This study presents a novel hemostatic CS/GEL cryogel containing V. thapsus leaf extract that holds strong potential for future applications in NCH management.Article Burg-Aided 2D MIMO Array Extrapolation for Improved Spatial Resolution(MDPI, 2025-10-12) Bekar, Muge; Bekar, Ali; Pirkani, Anum; Baker, Christopher John; Gashinova, MarinaIn this paper, the extrapolation of a 2D multiple-input multiple-output (MIMO) array is proposed using the Burg algorithm to achieve higher angular resolution beyond that of the corresponding 2D MIMO virtual array. The main advantage of such an approach is that it allows us to dramatically decrease both the physical size and the number of antenna elements of the MIMO array. The performance and limitations of the Burg algorithm are examined through both simulation and experimentation at 77 GHz. The experimental methodology used to acquire 3D data of range, azimuth and elevation information with the 1D MIMO off-the-shelf radar is described. Using this method, the performance of the proposed array can be tested experimentally, especially at frequencies where it is desired to assess the antenna response prior to fabricating the antenna.Correction Correction: Engineering Novel Features for Diabetes Complication Prediction Using Synthetic Electronic Health Records(Frontiers Media S.A., 2025-08-29) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikArticle Citation - WoS: 26Citation - Scopus: 33miRmoduleNet: Detecting miRNA-mRNA Regulatory Modules(Frontiers Media S.A., 2022-04-12) Yousef, Malik; Goy, Gokhan; Bakir-Gungor, BurcuIncreasing evidence that MicroRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis.Article Citation - WoS: 20Citation - Scopus: 24miRdisNET: Discovering MicroRNA Biomarkers That Are Associated With Diseases Utilizing Biological Knowledge-Based Machine Learning(Frontiers Media S.A., 2023-01-12) Jabeer, Amhar; Temiz, Mustafa; Bakir-Gungor, Burcu; Yousef, MalikDuring recent years, biological experiments and increasing evidence have shown that MicroRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified MicroRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: .Article Citation - WoS: 26Citation - Scopus: 31miRcorrNet: Machine Learning-Based Integration of miRNA and mRNA Expression Profiles, Combined with Feature Grouping and Ranking(PeerJ Inc., 2021-05-19) Yousef, M.; Göy, G.; Mitra, R.; Eischen, C.M.; Jabeer, A.; Bakir-Güngör, B.A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/ malikyousef/miRcorrNet. © 2021 Elsevier B.V., All rights reserved.Article Citation - WoS: 24Citation - Scopus: 26Wireless Measurement of Elastic and Plastic Deformation by a Metamaterial-Based Sensor(MDPI, 2014-10-20) Ozbey, Burak; Demir, Hilmi Volkan; Kurc, Ozgur; Erturk, Vakur B.; Altintas, AyhanWe report remote strain and displacement measurement during elastic and plastic deformation using a metamaterial-based wireless and passive sensor. The sensor is made of a comb-like nested split ring resonator (NSRR) probe operating in the near-field of an antenna, which functions as both the transmitter and the receiver. The NSRR probe is fixed on a standard steel reinforcing bar (rebar), and its frequency response is monitored telemetrically by a network analyzer connected to the antenna across the whole stress-strain curve. This wireless measurement includes both the elastic and plastic region deformation together for the first time, where wired technologies, like strain gauges, typically fail to capture. The experiments are further repeated in the presence of a concrete block between the antenna and the probe, and it is shown that the sensing system is capable of functioning through the concrete. The comparison of the wireless sensor measurement with those undertaken using strain gauges and extensometers reveals that the sensor is able to measure both the average strain and the relative displacement on the rebar as a result of the applied force in a considerably accurate way. The performance of the sensor is tested for different types of misalignments that can possibly occur due to the acting force. These results indicate that the metamaterial-based sensor holds great promise for its accurate, robust and wireless measurement of the elastic and plastic deformation of a rebar, providing beneficial information for remote structural health monitoring and post-earthquake damage assessment.