The reliability of national videos related to the kidney stones on YouTube

Objective: Kidney stones are one of the most common disorders of the urinary tract. With increasing awareness, a larger proportion of patients are seeking medical knowledge from the Internet. In present study, the features, reliability and efficacy of videos on YouTube related to the treatment of kidney stones were evaluated. Material and methods: In December 2014, YouTube was searched using keywords “nephrolithiasis”; “renal calculi”; “renal stones”; and “kidney stones” for videos uploaded containing relevant information about the disease. Only videos in Turkish were included in the study. Two physician viewers watched each video and classified them as useful, partially useful and useless according to European Association of Urology (EAU) Guidelines. The source, length, number of views, number of favourable opinions, and days since uploaded date of the all videos were evaluated. Results: A total of 600 videos were analysed The median length of videos was 6.7±10.4 (median: 3, IQR: 0.03-58) minutes . Each video was viewed at an average of 2368 (min: 11, max: 97133) times. Most of the videos (32.8%) were created by academicians and physicians. Nearly half (47.4%) of the videos were uploaded in 2014. The majority of the videos (62.5%) contained information for treatment. Percutaneous nephrolithotomy and ureterorenoscopy were the most common treatment modalities (32.8% and 28.0%, respectively) in these videos. A statistically significant difference was not detected between view numbers and source of videos (p=0.87). However, there was a statistically significant difference between usefulness to the viewers and source of videos. Hospital -based videos were detected to be more useful (p=0.000). Conclusion: As a result, videos that would be prepared in internet environment by professional individuals or organizations in a way which would attract attention and be easily comprehended by the public could contribute to the knowledge and education of our society about the stone disease which is commonly seen in our country. © 2016 by Turkish Association of Urology

Ağrı algısının somatosensöriyel kortekste fNIRS kullanılarak sağlıklı ve fibromiyalji hasta popülasyonlarında incelenmesi.

In this study, we investigated the difference in hemodynamic responses between fibromyalgia (FM) and healthy controls via functional near infrared spectroscopy (fNIRS) during application of painful stimulus and transcutaneous electrical nerve stimulation (TENS). We collected several clinical data (pain threshold, Beck Depression Inventory (BDI) score, Fibromyalgia Impact Questionnaire (FIQ) score, pain ratings) before and during the experiment. After data collection, we analyzed it using general linear model (GLM) and we applied classification methods to determine which cortical structures are important in discriminating healthy and patient groups. Our study showed that TENS effect was observed in both hands of healthy controls, but only left hand of FM patients. However, there is an opposite effect observed when the right hand of FM patients is stimulated. These findings indicate that the pain perception mechanism in FM syndrome needs further investigation since the outcome of the TENS treatment differs with respect to hands. When classification is done using SVM using features from the painful stimulation experiment, an accuracy of %90 is observed in distinguishing patients from healthy controls.  Ph.D. - Doctoral Progra

FILTERING OF FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY EIGENVALUE BASED METHODS

Functional Near Infrared Spectroscopy is used in neuroimaging studies to observe the oxyhemoglobin (HB02) and deoxyhemoglobin (HB) changes. Blood oxygen level dependency (BOLD) signal that is collected by using this system shows response in related region in brain against an applied stimulus. Therefore in these signals, signal to noise ratio (SNR) is quite important to decide the behavior of brain in related region In this study, fNIRS data was filtered by using eigenvalue based methods such as Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (tSVD). Using SNR and Autoregressive (AR) power spectrum performance results were compared

Filtering of functional near infrared spectroscopy signals by eigenvalue based methods

Functional Near Infrared Spectroscopy is used in neuroimaging studies to observe the oxyhemoglobin (HB02) and deoxyhemoglobin (HB) changes. Blood oxygen level dependency (BOLD) signal that is collected by using this system shows response in related region in brain against an applied stimulus. Therefore in these signals, signal to noise ratio (SNR) is quite important to decide the behavior of brain in related region In this study, fNIRS data was filtered by using eigenvalue based methods such as Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (tSVD). Using SNR and Autoregressive (AR) power spectrum performance results were compared

Extracting, computing, coordination: what does a triphasic ERP pattern say about language processing?

The current study aims at contributing to the interpretation of the most prominent language-related ERP effects, N400 and P600, by investigating how neural responses to congruent and incongruent sentence endings vary, when the language processor processes the full array of the lexico-syntactic content in verbs with three affixes in canonical Turkish sentences. The ERP signals in response to three different violation conditions reveal a similar triphasic (P200/N400/P600) pattern resembling in topography and peak amplitude The P200 wave is interpreted as the extraction of meaning from written.form by generating a code which triggers the computation of neuronal ensembles in the distributed LTM (N400). The P600 potential reflects the widely distributed coordination process of activated neuronal patterns of semantic and morphosyntactic cues by connecting the generated subsets of these patterns and adapting them into the current context. It further can be deduced that these ERP components reflect cognitive rather than linguistic processes. © 2021 Informa UK Limited, trading as Taylor & Francis Group.WOS:000722538700001Scopus - Affiliation ID: 60105072Science Citation Index Expanded - Social Sciences Citation IndexQ1 - Q2Article; Early AccessUluslararası işbirliği ile yapılmayan - HAYIRNovember2021YÖK - 2021-22Kası

Binary Classification Using Neural and Clinical Features: An Application in Fibromyalgia With Likelihood-Based Decision Level Fusion

Eken, Aykut/0000-0002-7023-7930; Gokcay, Didem/0000-0002-1101-0306WOS: 000474575600015PubMed: 29994341Among several features used for clinical binary classification, behavioral performance, questionnaire scores, test results, and physical exam reports can be counted. Attempts to include neuroimaging findings to support clinical diagnosis are scarce due to difficulties in collecting such data, as well as problems in integration of neuroimaging findings with other features. The binary classification method proposed here aims to merge small samples from multiple sites so that a large cohort, which better describes the features of the disease can be built. We implemented a simple and robust framework for detection of fibromyalgia, using likelihood during decision level fusion. This framework supports sharing of classifier applications across clinical sites and arrives at a decision by fusing results from multiple classifiers. If there are missing opinions from some classifiers due to inability to collect their input features, such degradation in information is tolerated. We implemented this method using functional near infrared spectroscopy (fNIRS) data collected from fibromyalgia patients across three different tasks. Functional connectivity maps are derived from these tasks as features. In addition, self-reported clinical features are also used. Five classifiers are trained using k nearest neighborhood (kNN), linear discriminant analysis (LDA), and support vector machine (SVM). Fusion of classification opinions from multiple classifiers based on likelihood ratios outperformed individual classifier performances. When 2, 3, 4, and 5 different classifiers are fused, sensitivity, and specificity figures of 100% could be obtained based on the choice of the classifier set

Türkçe’nin Işlemlenmesi Sırasında Türkiye ve Almanya’daki Konuşuculardaki Beyin Tepkilerinin Olaya Ilişkin Potansiyeller Tekniği kullanarak Karşılaştırılması.

Literatürde, dillerin beyinde işlemlenmesi sırasında tanımlanmış standart potansiyellerin oluşabildiği ile yaygın bir görüş vardır. Ancak Avrupa dilleri dışında farklı dil tipolojisine sahip diller ile ilgili henüz çok fazla araştırma olmadığı için, Avrupa dillerinin işlemlenmesi sırasında oluşan potansiyellerin tanımı standart olarak kabul ediliyordu. Türkçe’nin dil tipolojisi ile benzerlik göstern Fince ve Korece ile ilgili de bir iki araştırma bulunmaktadır. Dolayısıyla, bu tür dillerin araştırılması, uluslararası alan çalışmalarında ve literatürde çok ilgi görmektedir, çünkü standart olarak tanımlanmış olan potansiyellerin ve sinirsel işlemleme yapısının bütün diller için aynı olmadığı ortaya çıkmaya başladı. Ancaka farkların neye bağlı ve hangi durumlarda ortaya çıktığı henüz tartışma konusudur.Bu çalışma ile, ilk çalışmamızın bulgularını genişletmek ve Türkçe’nin beyindeki işlemleme süreçleri ile ilgili yeni bulgular ortaya koyabilmek için, Türkçe’nin Almanya’da yaşayan ve iki dilli büyüyen Türkler’de arştırlmaya çalışılacaktır. Bunun için Türkiye’de yaşayan Türkçe anadili konuşucular için kullandığımız paradigmayı Almanya’da yaşayan iki dilli Türk kökenli katımlımcılarda uygulamayı planlamaktayız. Bu çalışmanın amacı, Türkçe’nin birinci dil/ anadili işlemlenmesi ile Türkçe’nin ikinci dil ile birlikte çocuklukta edinen kişilerdeki beyin tepkilerini ölçelerek işlemleme mekanizmalarını saptamaktır. Bu iki grup karşılaştırılırken, özellikle işlemleme hızı ve nöronal kay

Sigara Kullanma Durumunun Çoklu Fizyolojik Ölçümler Ve Makine Öğrenmesi Teknikleri Kullanılarak Tahmini

Sigara kullanımı toplumlarda gerek sağlık gerek ekonomik açıdan ciddi kayıplara sebep olmaktadır.Kullanım seviyesinin ölçümünde bir altın standart bulunmamasına rağmen, Fagerstörm NikotinBağımlılık Testi (Fagerstörm Test for Nicotine Dependency – FTND) ve HONC (Hooked on NicotineChecklist) gibi geleneksel testler ve çeşitli nörogörüntüleme yaklaşımları kişinin sigara içmedurumunun seviyesi hakkında bir bilgi vermektedir. Bu çalışmada, objektif bir veri olan fizyolojikparametrelerin subjektif bir veri olan bağımlılık testlerinin yerine kullanım seviye tespitinde yeni biryaklaşım olarak kullanılabileceğini göstermek amaçlanmıştır. Bu amaçla çeşitli seviyelerdeki sigarakullanıcılarından fizyolojik sinyaller (elektrokardiyogram (EKG), Solunum ve Fotopletismografi)toplanmıştır. Bu sinyallerden elde edilen çeşitli öz niteliklerden makine öğrenmesi yaklaşımlarıkullanılarak katılımcılar düşük seviye veya yüksek seviye olarak tahmin edilmeye çalışılmıştır.Çalışma için önceden FTND bağımlılık testine giren değişik kullanım seviyelerinde 95 katılımcı alınıpbu kişilerden sırasıyla 50 saniyelik EKG, solunum ve fotopletismografi sinyalleri alınmıştır. Öznitelikçıkarımından sonra, parametre optimizasyonu ve sınıflandırma içeren 10 kat içiçe çapraz geçerlilikgerçekleştirilmiştir. Yapılan sınıflandırma sonucunda destek vektör makinesi kullanılarak %93,diskriminant analizi kullanılarak ise %91 doğruluk başarımı elde edilmiştir. Bu sonuçlar, yukarıdabelirtilen fizyolojik parametrelerin makine öğrenmesi algoritmaları aracılığı ile sigara kullanımdurumunun tespitinde kullanılabileceğini göstermektedir.Smoking causes severe economic and health losses in communities. Despite the lack of a gold standard for the measurement of usage level, conventional tests such as Fagerstörm Test for Nicotine Dependency (FTND), Hooked on Nicotine Checklist (HONC) and various neuroimaging approaches provide information about the level of smoking status. In this study, usage of objective physiological parameters was proposed as a new approach to detect level of status instead of subjective status tests. In order to achieve this physiological signals (i.e.., electrocardiogram (ECG), respiration and photoplestimography) were acquired from participants from different smoking status levels. Participants’ smoking status levels were predicted as high dependent and low dependent from features extracted from these physiological signals using machine learning approaches. For this study, 95 university students with different levels of smoking status were recruited according to FTND test results and ECG, respiration and photopletismography signals were acquired respectively for 50 seconds to provide data for machine learning models. After feature extraction, a 10 fold nested- cross validation that includes hyperparameter optimization and classification was performed. According to the classification results, 93 % accuracy and 91 % accuracy were found by using Support Vector Machine and Discriminant Analysis respectively. These results revealed that physiological parameters might be used to predict smoking status via machine learning algorithms

Differential Efficiency Of Transcutaneous Electrical Nerve Stimulation In Dominant Versus Nondominant Hands In Fibromyalgia: Placebo-Controlled Functional Near-Infrared Spectroscopy Study

Using functional near-infrared spectroscopy (fNIRS), modulation of hemodynamic responses by transcutaneous electrical nerve stimulation (TENS) during delivery of nociceptive stimulation was investigated in fibromyalgia (FM) patients and healthy controls for both hands. Two experiments were conducted: (1) median nerve stimulation with TENS and (2) painful stimulation using electronic von Frey filaments with TENS/placebo TENS. Mean Delta HbO(2) brain activity was compared across groups and conditions using factorial ANOVA. Dominant (right) hand stimulation indicated significant interactions between group and condition in both hemispheres. Post hoc results revealed that FM patients showed an increased activation in "pain + TENS" condition compared to the "pain + placebo TENS" condition while the brain activity patterns for these conditions in controls were reversed. Left-hand stimulation resulted in similar TENS effects (reduced activation for "pain + TENS" than " pain + placebo TENS") in both groups. TENS effects in FM patients might be manipulated by the stimulation side. While the nondominant hand was responsive to TENS treatment, the dominant hand was not. These results indicate that stimulation side might be an effective factor in FM treatment by using TENS. Future studies are needed to clarify the underlying mechanism for these findings. (c) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).WoSScopu