TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Research Project MRD Biyoçip: Minimal Rezidüel Hastalığın Güvenilir ve Basit Bir Yolla İzlenmesi(2019) Akar, Ünal; Karakukcu, Musa; Deniz, Gunnur; Kupesiz, Osman Alphan; Cinar, Suzan; Yilmaz, Bulent; Kaya, ZuhreTürkiye Halk Sağlığı Kurumu verilerine göre Türkiye?de çocukluk çağında en sık görülen kanser türü lösemidir ve lösemi türleri arasında Akut Lenfoid Lösemi (ALL) 15 yaş altındaki çocuklarda gözlenen lösemilerin %80?inini oluşturur. Lösemiden korunmanın kesin bir yöntemi şu an için bilinmemektedir ve lösemi hastalarına uygulanan kemoterapi (ilaç tedavisi), radyoterapi, kemik iliği nakli ve immünoterapi gibi farklı tedaviler mevcuttur. Akut lenfoblastik lösemi hastalarının tedavi sürecinde uygulanan kemoterapi her hastaya aynı şekilde etki etmemekte; bazı hastalar tedaviye yanıt verirken bazı hastalarda lösemik hücreler (blastlar) kemoterapiye direnç göstermektedir. Sonuçta tedaviden kaçan bu lösemik blastlar hastalık tekrarlarına (relapslara) neden olabilmektedirler. Tedavinin 15. gününde incelenen minimal rezidüel (kalıntı) hastalık (minimal residuel disease, MRD) akut lösemi hastalarında sağ kalımın en önemli göstergesi olup uluslararası tedavi protokollerinde standart olarak kullanılmaktadır. Bu protokollere göre MRD pozitif tespit edilir ise kemoterapi tedavisi daha da yoğunlaştırılmaktadır. MRD ölçümü günümüzde akım sitometrisi (flow cytometry FC) ve polimeraz zincir reaksiyonu (PCR) ile yapılabilmektedir. Her iki yöntemde de sonuç almak uzun vakit almakta, her iki yöntemin de maliyeti yüksek olup, sadece uzman kullanıcılar tarafından akredite olmuş referans laboratuvar ortamlarında gerçekleştirilebilmektedir. Alt yapı yetersizliği ve yüksek maliyetlerden dolayı tedavi edilen ALL hastalarının çoğunluğunda MRD tespiti mümkün olamamaktadır. Oysa kemoterapi gören hastalarda, relapslara neden olan hücreler için MRD taraması ile, tedavinin seyri değişebilecek, her hastaya uygun ilaç dozajı ayarlanabilecek ve ileri dönemde relapslar azaltılabilecektir. Günümüzde MRD testi için kullanılan laboratuvar yöntemleri kadar hassas, fakat maliyeti daha düşük biyosensör cihazların geliştirilmesi lösemi tedavisinde çığır açacak potansiyele sahiptir. Mikro/nano teknoloji tabanlı biyoçipler üreterek alternatif bir metot geliştirerek, hastaların tedavi sürecini iyileştirmek, hekimlere büyük kolaylık sağlamak, ülkemize katma değeri yüksek bir ürün kazandırmak mümkündür. Geliştirilmek istenen biyoçip ile B öncül ALL hastalarındaki kanserli hücrelerin kemoterapi sürecindeki durumları ve tedaviye gösterdikleri yanıt izlenebilecek, bu da hastalara en uygun ilaç dozajının ayarlanarak kişiye özel tedavi uygulanmasını mümkün kılabilecektir.Article Citation - WoS: 2Citation - Scopus: 2Prediction of Preference and Effect of Music on Preference: A Preliminary Study on Electroencephalography from Young Women(Tubitak Scientific & Technological Research Council Turkey, 2019-03-01) Yilmaz, Bulent; Gazeloglu, Cengiz; Altindis, FatihNeuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.Article Citation - WoS: 4Citation - Scopus: 5Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition(Istanbul Univ-Cerrahapasa, 2018-08-03) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals. Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.Article Citation - WoS: 5Citation - Scopus: 10A New Tool for QT Interval Analysis During Sleep in Healthy and Obstructive Sleep Apnea Subjects: A Study on Women(Tubitak Scientific & Technological Research Council Turkey, 2013) Kaya, Kemal Alican; Yilmaz, BulentBy monitoring the Q wave/T wave (QT) interval computed from electrocardiography (ECG) signals during sleep, it is possible to create a link between the ventricular repolarization and sleep stages. In this study, we aimed to find a robust and simple approach to automatically determine the fiducials on each 30-s sleep epoch, such as the Q, R, and T-end points, on long sleep ECG recordings in order to statistically analyze the effect of obstructive sleep apnea (OSA) and sleep stages on QT intervals. This is a retrospective study in which the ECG data extracted from the polysomnography recordings of 7 healthy women and 5 women with OSA, acquired in a sleep laboratory, were used. Experts annotated the sleep stage and OSA presence information for each 30-s epoch. Later, we visually selected epochs with clean signals from a total of 8324 epochs. On the selected epochs, we determined R peaks on each heartbeat, and by aligning each ECG portion corresponding to a heartbeat using those R points, we computed an average ECG signal for each epoch. On the average ECG signals, we developed a novel approach to find the Q and T-end points. With the help of Bazzet's formula, we computed the corrected QT interval (QTc) values for each epoch using the QT and the median RR interval. Finally, we analyzed the QTc values for the different sleep stages and healthy or OSA groups. We employed statistical approaches such as the Mann-Whitney U test, Freidman's test, and the Wilcoxon signed-rank test. As a result of this study, we found that OSA has a prolongation effect on the total duration of the ventricular depolarization and repolarization. We also observed that the QTc values computed in each sleep stage were significantly different between the healthy and OSA groups. Additionally, we discovered that within the healthy group, the QTc values were distinctive in the different sleep stages.