WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Editorial Editors' Introduction: Fall 2025(Cambridge Univ Press, 2025-10-28) Dincer, Evren M.; Yukseker, Deniz; Kolluoglu, BirayArticle An Extension of Lucas's Theorem(indian Nat Sci Acad, 2025-10-31) Cinkir, Zubeyir; Ozturkalan, AysegulWe give elementary proofs of some congruence criteria to compute binomial coefficients modulo a prime number. These criteria are analogues to the symmetry property of binomial coefficients. We give extended version of Lucas's Theorem by using those criteria. We give applications of these criteria by describing a method to derive identities and congruences involving sums of binomial coefficients.Article Citation - WoS: 1Citation - Scopus: 1Interaction of Inula Viscosa (L.) Aiton with IBA1 via Rosmarinic Acid and Rutin: Insights from Computational Models and Biological Effects(Wiley-VCH verlag GmbH, 2025-10-29) Aktas Pepe, Nihan; Acar, Busra; Ceylan Ekiz, Yagmur; Senol, Ayse Merve; Semiz, Gurkan; Sen, Alaattin; Celik Turgut, GurbetInula viscosa (L.) Aiton is a traditional medicinal plant extensively utilized in Mediterranean nations for the treatment of rheumatic pain, inflammatory disorders, diabetes, anemia, and cancer. This study further explored its anti-inflammatory mechanisms through the highest components, chlorogenic acid, rosmarinic acid, and rutin, on the expression of the ionized calcium-binding adapter molecule 1 (Iba1) on monocyte-derived macrophage-like cells. Iba1 is known to contribute pathogenesis of diverse inflammatory diseases. HPLC analysis identified 13 major phenolic compounds, with rosmarinic acid, chlorogenic acid, and rutin as major components. The aqueous extract of the plant and its major components exhibited dose-dependent antiproliferative activity on pTHP-1, RAW264.7, and PCS-201-012 cells. Immunofluorescence staining revealed a significant reduction in Iba1 protein expression, which is associated with inflammation, at the high dose of I. viscosa and rutin. Molecular docking studies indicated that rosmarinic acid and rutin had the strongest predicted interactions with Iba1, with docking scores of -12.403 and -12.301 kcal/mol and MM/GBSA binding energies of -64.47 and -84.20 kcal/mol, respectively. I. visoca and its major components were observed to significantly suppress iNOS activity in LPS-stimulated cells; these findings were also supported by RT-PCR results. Treatment with the high dose of I. viscosa resulted in 9.45% necrotic cells and caused cell cycle arrest in the S phase (59.2 +/- 5.23%). This suggests that it may potentially reduce the proliferation of activated macrophages. In the fibroblast migration assays, the relative wound closure rate was found to be significant 27.06 +/- 18.09% at the low dose of I. viscosa and 31.59 +/- 22.42% at the high dose of I. viscosa. Although the relatively low wound closure rate limits tissue repair, it may benefit chronic wounds and fibrosis by suppressing excessive cell proliferation and inflammation. These results suggest that I. viscosa is a promising natural source of bioactive compounds with potential applications in anti-inflammatory drug development.Conference Object Citation - WoS: 1Citation - Scopus: 1Oscillator Phase Noise Impact on Monostatic/Bistatic Space-Borne Sub-THz ISAR(IEEE, 2025-05-21) Bekari, Ali; Gashinova, Marina; Bekar, Muge; Martorellai, Marco; Antonioni, Michail; Bekar, Ali; Martorella, Marco; Antoniou, MichailThis study develops an oscillator phase noise model and analyzes its effects on the performance of spaceborne monostatic and bistatic Inverse Synthetic Aperture Radar (B-ISAR) systems operating at the sub-THz band. The B-ISAR study is of current importance as it can provide a basis for distributed space-based ISAR to enable persistent co-operative space domain awareness (Co-SDA).Article Tuning Mechanical Performance of PCL Scaffolds: Influence of 3D Bioprinting Parameters, Polymer Concentration, and Solvent Selection(IOP Publishing Ltd, 2025-09-01) Ceylan, Saniye Aylin; Baltacioglu, Mehmet Furkan; Bal, Burak; Bayram, Ferdi Caner; Isoglu, Ismail AlperThe mechanical performance of three-dimensional (3D) bioprinted scaffolds is susceptible to printing parameters and material formulation. In this study, poly (epsilon-caprolactone) (PCL) scaffolds were fabricated using four different polymer concentrations (10%, 25%, 50%, and 75% w/v) to investigate how these variations, along with process parameters, influence mechanical behavior. Maintaining the structural integrity of bioprinted constructs requires careful optimization of polymer concentration and precise control over parameters such as printing speed, pressure, and infill density. Tensile tests were conducted to evaluate the effects of these variables. Among the tested conditions, a 50% (w/v) concentration allowed for a broader operational window, enabling fabrication across a range of printing speeds and pressures. At a printing speed of 5 mm s-1, PCL-DCM exhibited a Young's modulus of 39.0 MPa, while PCL-CF samples printed at 10 mm s-1 achieved the highest modulus of 32.0 MPa. Notably, when the printing speed was kept constant, applying higher pressures led to an increase in Young's modulus, suggesting that pressure plays a key role in enhancing scaffold stiffness. When comparing the 50% and 75% (w/v) polymer concentrations, the 50% (w/v) formulation stood out by offering both higher elongation and greater stiffness, which makes it particularly suitable for load-bearing applications. These findings provide a quantitative framework for optimizing extrusion-based bioprinting of PCL scaffolds, with implications for customized biomedical implants and regenerative medicine.Article Citation - WoS: 1Citation - Scopus: 1Prediction of the Diffusible Hydrogen Concentration After Electrochemical Charging Utilizing Artificial Intelligence(IOP Publishing Ltd, 2025-09-01) Sivesoglu, Abdurrahman; Li, Yang; Bal, BurakThe concentration of diffusible hydrogen in a material is of high importance as it helps to predict the hydrogen embrittlement effect in the material, and the amount of mechanical properties' degradation after reaching a critical concentration. Despite that, a simple experimental setup is not available to measure hydrogen concentration at service. In this paper, a multi-layer perceptron (MLP) model is developed using weight initialization, which can estimate the diffusible hydrogen concentration of Face-Centred-Cubic (FCC) metals after electrochemical charging. The input properties of the model include the electrochemical charging parameters of current density, temperature, and charging time as well as the grain size of the specimen. The MLP model with and without the weight initialization was validated and tested with unseen test dataset. The model in both cases showed an excellent predictive performance with a higher accuracy and faster convergence when using weight initialization. A linear correlation of 89% between the experimental and predicted hydrogen concentration was observed. This demonstrates that for the family of FCC metals under electrochemical charging, the estimation of diffusible hydrogen concentration is a feasible path for material safety design analysis.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.