WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 14Citation - Scopus: 15Triterpenoids and Steroids Isolated from Anatolian Capparis Ovata and Their Activity on the Expression of Inflammatory Cytokines(Taylor & Francis Ltd, 2020-01-01) Gazioglu, Isil; Semen, Sevcan; Acar, Ozden Ozgun; Kolak, Ufuk; Sen, Alaattin; Topcu, GulactiContext CapparisL. (Capparaceae) is grown worldwide. Caper has been used in traditional medicine to treat various diseases including rheumatism, kidney, liver, stomach, as well as headache and toothache. Objective To isolate and elucidate of the secondary metabolites of theC. ovataextracts which are responsible for their anti-inflammatory activities. Materials and methods Buds, fruits, flowers, leaves and stems ofC. ovataDesf. was dried, cut to pieces, then ground separately. From their dichloromethane/hexane (1:1) extracts, eight compounds were isolated and their structures were elucidated by NMR, mass spectroscopic techniques. The effects of compounds on the expression of inflammatory cytokines in SH-SY5Y cell lines were examined by qRT-PCR ranging from 4 to 96 mu M. Cell viability was expressed as a percentage of the control, untreated cells. Results This is a first report on isolation of triterpenoids and steroids fromC. ovatawith anti-inflammatory activity. One new triterpenoid ester olean-12-en-3 beta,28-diol, 3 beta-pentacosanoate (1) and two new natural steroids 5 alpha,6 alpha-epoxycholestan-3 beta-ol (5) and 5 beta,6 beta-epoxycholestan-3 beta-ol (6) were elucidated besides known compounds; oleanolic acid (2), ursolic acid (3), beta-sitosterol (4), stigmast-5,22-dien-3 beta-myristate (7) and bismethyl-octylphthalate (8). mRNA expression levels as EC(10)of all the tested seven genes were decreased, particularly CXCL9 (19.36-fold), CXCL10 (8.14-fold), and TNF (18.69) by the treatment of 26 mu M of compound1on SH-SY5Y cells. Discussion and conclusions Triterpenoids and steroids isolated fromC. ovatawere found to be moderate-strong anti-inflammatory compounds. Particularly, compounds1and3were found to be promising therapeutic agents in the treatment of inflammatory and autoimmune diseases.Article Citation - WoS: 8Citation - Scopus: 11Stem Cells Combined 3D Electrospun Nanofibrous and Macrochannelled Matrices: A Preliminary Approach in Repair of Rat Cranial Bones(Taylor & Francis Ltd, 2019-04-03) Isoglu, Ismail Alper; Bolgen, Nimet; Korkusuz, Petek; Vargel, Ibrahim; Celik, Hakan Hamdi; Kilic, Emine; Piskin, ErhanRepair of cranial bone defects is an important problem in the clinical area. The use of scaffolds combined with stem cells has become a focus in the reconstruction of critical-sized bone defects. Electrospinning became a very attracting method in the preparation of tissue engineering scaffolds in the last decade, due to the unique nanofibrous structure of the electrospun matrices. However, they have a limitation for three dimensional (3D) applications, due to their two-dimensional structure and pore size which is smaller than a cellular diameter which cannot allow cell migration within the structure. In this study, electrospun poly(epsilon-caprolactone) (PCL) membranes were spirally wounded to prepare 3D matrices composed of nanofibers and macrochannels. Mesenchymal stromal/stem cells were injected inside the scaffolds after the constructs were implanted in the cranial bone defects in rats. New bone formation, vascularisation and intramembranous ossification of the critical size calvarial defect were accelerated by using mesenchymal stem cells combined 3D spiral-wounded electrospun matrices.Article Citation - WoS: 20Citation - Scopus: 26Medical Infrared Thermal Image Based Fatty Liver Classification Using Machine and Deep Learning(Taylor & Francis Ltd, 2023-01-10) Ozdil, Ahmet; Yilmaz, BulentNon-alcoholic fatty liver disease (NAFLD) causes accumulation of excess fat in the liver affecting people who drink little to no alcohol. Non-alcoholic steatohepatitis (NASH) is an aggressive form of fatty liver disease (inflammation in the liver), may progress to cirrhosis and liver failure. Liver function tests, ultrasound (US) and magnetic resonance imaging (MRI) are used to help diagnose and monitor liver disease or damage. In this study, the feasibility of medical infrared thermal imaging (MITI) in automatic detection of NAFLD was investigated, and 167 MITI images (44 positive) from 32 patients (7 positive) were evaluated using image processing and classification methods. Convolutional neural network (CNN) architectures and texture analysis methods were used in the feature selection phase. After feature selection and binary classification, the highest values from different setups for recall, f-score, specificity, accuracy, and area-under-curve (AUC) were 1.00, 1.00, 0.83, 1.0, 0.94, and 0.92, respectively. The highest values were achieved by CNN based methods on different datasets, however, texture analysis method performed lower. Here, it is shown that some of the CNN architectures have high potential on extracting features from thermal images. Finally, machine and deep learning approaches can be combined in detecting NAFLD using infrared thermal images.Article Citation - WoS: 6Citation - Scopus: 6Low-Speed Bending Impact Behaviour of Adhesively Bonded Dissimilar Single-Lap Joints(Taylor & Francis Ltd, 2021-10-11) Atahan, M. Gokhan; Apalak, M. KemalThis study investigates the low-speed bending impact behaviour of adhesively bonded dissimilar single-lap joints and the effects of both strength and plastic deformation capability of adherend material on adhesive failure. Dissimilar adhesive single-lap joint specimens, such as Al 2024-T3 (top adherend)-Al 5754-0 (bottom) and Al 5754-0 (top)-Al 2024-T3 (bottom), were tested at two impact energy levels (3 and 11 J) for two overlap lengths (25 and 40 mm). The progressive failure analysis of the adhesive layer was also conducted by the non-linear explicit finite element method. The adhesive layer was modelled with a 3D cohesive layer along with the upper and lower adhesive interfaces and a non-linear continuum adhesive region between two cohesive layers. The continuum adhesive region had elasto-plastic adhesive properties whilst the cohesive layers obeyed 3D cohesive rules. The experimental and predicted contact force-time, contact force-displacement diagrams, axial separation lengths of the failed adhesive region, permanent deflection of the bonded region, fracture surfaces were in good agreement. The strength and plastic deformation capability of adherend materials and impact energy levels affected the progressive adhesive failure behaviour. The proposed finite element model was successful reasonably in predicting the initiation and propagation of the adhesive failure.Article Citation - WoS: 12Citation - Scopus: 12Low-Speed Bending Impact Behavior of Adhesively Bonded Single-Lap Joints(Taylor & Francis Ltd, 2016-12-26) Atahan, M. Gokhan; Apalak, M. Kemal; Gokhan Atahan, M.; Kemal Apalak, M.This study addresses the low-speed impact behavior of adhesively bonded single-lap joints. An explicit dynamic finite element analysis was conducted in order to determine the damage initiation and propagation in the adhesive layers of adhesive single-lap joints under a bending impact load. A cohesive zone model was implemented to predict probable failure initiation and propagation along adhesive-adherend interfaces whereas an elasto-plastic material model was used for the adhesive zone between upper and lower adhesive interfaces as well as the adherends. The effect of the plastic deformation ability of adherend material on the damage mechanism of the adhesive layer was also studied for two aluminum materials Al 2024-T3 and Al 5754-0 having different strength and plastic deformation ability. The effects of impact energy (3 and 11 J) and the overlap length (25 and 40 mm) were also investigated. The predicted contact force-time, contact force-central displacement variations, the damage initiation and propagation mechanism were verified with experimental ones. The SEM and macroscope photographs of the adhesive fracture surfaces were similar to those of the explicit dynamic finite element analysis.Article Citation - WoS: 39Citation - Scopus: 38Loading-Rate Effect on Tensile and Bending Strength of 3D-Printed Polylactic Acid Adhesively Bonded Joints(Taylor & Francis Ltd, 2021-05-18) Atahan, M. Gokhan; Apalak, M. KemalAdditive manufacturing provides the production of many machine parts and components with complex geometries. The adhesive bonding technique can be alternative method for joining parts produced with additive manufacturing. This experimental study investigates the applicability of the adhesive bonding technique for PLA (polylactic acid) adherends produced with additive manufacturing and especially the effects of loading rate on the strength of 3D-printed PLA adhesive single-lap joints under tensile, three-point bending (with shear) and four-point bending (no shear effect) loadings. Both PLA and adhesive tensile test specimens exhibited a better strength but lower failure strain with increasing loading rate. PLA had better mechanical behaviour in the raster orientation than those in the layer-build direction. The strength of adhesive single-lap joints improved slightly with increasing loading rate for the tensile and three-point bending tests whilst a decrease of strength and an improvement of bending stiffness were observed for the four-point bending test. Failure initiated at the free edge of the top adherend-adhesive interface for all tests, and propagated along this interface for both bending tests whilst a sudden through-the-thickness failure of top adherend occurred for tensile load after a small interfacial damage propagation. The failure propagation appeared in a wavy form for the three-point bending test whilst it was along the top adherend-adhesive interface for the four-point bending test. Digital Image Correlation (DIC) method for tensile tests showed that the peeling and shear strains were more critical and concentrated around both free edges of adherend-adhesive interfaces; thus, at the right free edge of the top adherend-adhesive interface and at the left free edge of the bottom adherend-adhesive interface.Article Citation - WoS: 653Citation - Scopus: 739Flow Cytometry: Basic Principles and Applications(Taylor & Francis Ltd, 2016-01-14) Adan, Aysun; Alizada, Gunel; Kiraz, Yagmur; Baran, Yusuf; Nalbant, AytenFlow cytometry is a sophisticated instrument measuring multiple physical characteristics of a single cell such as size and granularity simultaneously as the cell flows in suspension through a measuring device. Its working depends on the light scattering features of the cells under investigation, which may be derived from dyes or monoclonal antibodies targeting either extracellular molecules located on the surface or intracellular molecules inside the cell. This approach makes flow cytometry a powerful tool for detailed analysis of complex populations in a short period of time. This review covers the general principles and selected applications of flow cytometry such as immunophenotyping of peripheral blood cells, analysis of apoptosis and detection of cytokines. Additionally, this report provides a basic understanding of flow cytometry technology essential for all users as well as the methods used to analyze and interpret the data. Moreover, recent progresses in flow cytometry have been discussed in order to give an opinion about the future importance of this technology.Article Citation - WoS: 8Citation - Scopus: 6Defect Classification of Composite Materials Using Transfer Learning Methods(Taylor & Francis Ltd, 2024-11-07) Gulsen, Abdulkadir; Kolukisa, Burak; Ozdemir, Ahmet Turan; Bakir-Gungor, Burcu; Gungor, Vehbi CagriNowadays, composite materials have become prevalent across various sectors, particularly finding usage in large-scale applications such as spaceships, automobiles, and aircrafts. The accurate detection of the defects in these materials is crucial, yet traditional methods often rely on human inspection, which is susceptible to errors. Recent advancements in machine learning have enabled defect detection using ultrasonic non-destructive testing methods. This paper introduces a new dataset named UNDT, which is obtained from the scans of 60 different composite materials, generating a total of 1150 images depicting both defective and non-defective areas. Several transfer learning methods are applied on the newly introduced UNDT dataset as well as the publicly available USimgAIST ultrasonic dataset. Comparative performance assessments illustrate the significance of utilising the transfer learning approach for defect classification on ultrasonic inspection images. Furthermore, the research emphasises the substantial benefits of employing these transfer learning methods. Notably, the DenseNet121 and VGG19 models achieve the highest accuracy rates, with 98.8% and 98.6% on the UNDT and USimgAIST datasets, respectively.Article Citation - WoS: 16Citation - Scopus: 18Automatic Body Part and Pose Detection in Medical Infrared Thermal Images(Taylor & Francis Ltd, 2021-06-29) Ozdil, Ahmet; Yilmaz, BulentAutomatisation and standardisation of the diagnosis process in medical infrared thermal imaging (MITI) is crucial because the number of medical experts in this area is highly limited.The current studies generally need manual intervention. One of the manual operations requires physician's determination of the body part and orientation. In this study automatic pose and body part detection on medical thermal images is investigated. The database (957 thermal images - 59 patients) was divided into four classes upper-lower body parts with back-front views. First, histogram equalization (HE) method was applied on the pixels only within the body determined using Otsu'sthresholding approach. Secondly, DarkNet-19 architecture was used for feature extraction, and principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) approaches for feature selection. Finally, the performances of various machine learning based classification methods were examined. Upper vs. lower body parts and back vs. front of upper body were classified with 100% accuracy, and back vs. front classification of lower body part success rate was 93.38%. This approach will improve the automatisation process of thermal images to group them for comparing one image with the others and to perform queries on the labeled images in a more user-friendly manner.Article Citation - WoS: 1Analysis of the in Vitro Nanoparticle-Cell Interactions via a Smoothing-Splines Mixed-Effects Model(Taylor & Francis Ltd, 2015-05-12) Dogruoz, Elifnur; Dayanik, Savas; Budak, Gurer; Sabuncuoglu, IhsanA mixed-effects statistical model has been developed to understand the nanoparticle (NP)-cell interactions and predict the rate of cellular uptake of NPs. NP-cell interactions are crucial for targeted drug delivery systems, cell-level diagnosis, and cancer treatment. The cellular uptake of NPs depends on the size, charge, chemical structure, and concentration of NPs, and the incubation time. The vast number of combinations of these variable values disallows a comprehensive experimental study of NP-cell interactions. A mathematical model can, however, generalize the findings from a limited number of carefully designed experiments and can be used for the simulation of NP uptake rates, to design, plan, and compare alternative treatment options. We propose a mathematical model based on the data obtained from in vitro interactions of NP-healthy cells, through experiments conducted at the Nanomedicine and Advanced Technologies Research Center in Turkey. The proposed model predicts the cellular uptake rate of silica, polymethyl methacrylate, and polylactic acid NPs, given the incubation time, size, charge and concentration of NPs. This study implements the mixed-model methodology in the field of nanomedicine for the first time, and is the first mathematical model that predicts the rate of cellular uptake of NPs based on sound statistical principles. Our model provides a cost-effective tool for researchers developing targeted drug delivery systems.