TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4
Browse
4 results
Search Results
Article Predictors of Gross Motor Function Level in Spastic Type Cerebral Palsy: a Retrospective Study(Turkey Assoc Physiotherapists, 2024-12-23) Ayaz Tas, Seda; Yakıt Yeşilyurt, Seda; Birinci Olgun, Tansu; Danis, Aysegul; Olgun, Tansu BİRİNCİ; Yeşi̇Lyurt, Seda YAKIT; Yakit Yebilyurt, Seda; Taş, Seda AYAZ; Birinci, TansuPurpose: This study was conducted to identify the determinants of gross motor function in patients with spastic-type Cerebral Palsy (CP) who received physiotherapy from a single center for two years. Methods: One hundred and eight children with spastic-type CP (mean age: 6.43 +/- 4.83 years) were evaluated twice, before and after the two-year physiotherapy. The outcomes were the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), Communication Function Classification System (CFCS), and Eating and Drinking Ability Classification System (EDACS). Binary logistic regression analysis was used to determine whether factors such as age, sex, topographical distribution, and levels of GMFCS, MACS, CFCS, and EDACS could predict the improvement in GMFCS level after the two-year physiotherapy. Results: The odds ratio of improvement in GMFCS level was found to vary significantly with the topographical distribution, CFCS level, and EDACS level (p<0.05). Compared to the children with CFCS Level I, children with CFCS Level II, Level III, and Level IV were 0.001, 0.005, and 0.006 times less likely to improve in GMFCS level, respectively. Similarly, children with EDACS Level III and Level IV were respectively 1.605 and 1.548 times less likely to improve in GMFCS level compared to those with Level I. Conclusion: CFCS and EDACS were significant predictors of gross motor function level in spastic- type CP. Healthcare professionals can use CFCS and EDACS to predict the progression of gross motor function levels, thereby providing more appropriate interventions and more realistic predictions.Article A Preliminary Study of Possible Fibrotic Role of Meprin Metalloproteases in Scleroderma Patients(Turkish League Against Rheumatism, 2021-12-31) Kocak, Ayse; Avsar, Aydan Koken; Harmancı, Duygu; Akdogan, Gul; Birlik, A. MerihObjectives: This study aims to investigate the possible fibrotic role of meprin metalloproteases and possible fibrotic effects of activator protein-1 (AP-1) in scleroderma patients. Patients and methods: Between April 2018 and April 2019, a total of 85 scleroderma patients (9 males, 76 females; mean age: 54.9 +/- 12.1 years; range, 22 to 80 years) who met the 2013 American College of Rheumatology/European League Against Rheumatism criteria and 80 healthy control individuals (10 males, 70 females; mean age 42.9 +/- 10.2 years; range, 19 to 65 years) were included. Patients' data and blood samples were collected. Messenger ribonucleic acid expressions of interleukin (IL)-6, AP-1 subunits, and tumor necrosis factor-alpha (TNF-alpha) were analyzed by quantitative real-time polymerase chain reaction. Serum meprin alpha and beta protein levels were analyzed using the enzyme-linked immunosorbent assay. Results: Meprin alpha and meprin beta protein levels increased in scleroderma patients. The AP-1 subunits (c-Fos, c-Jun), IL-6, and TNF-alpha increased in scleroderma patients, compared to controls. Conclusion: Our results provide evidence showing that increased meprins levels may be related to AP-1 levels and increased meprins levels may responsible for increased inflammatory TNF-alpha and IL-6 levels. All these data suggest meprins as promising therapeutic targets to restore the balance between inflammation and extracellular matrix deposition in scleroderma.Article Citation - WoS: 7Citation - Scopus: 10Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma Using Color Fundus Photography(Turkish Ophthalmological Soc, 2022-06-29) Atalay, Eray; Ozalp, Onur; Devecioglu, Ozer Can; Erdogan, Hakika; İnce, Türker; Yildirim, NilgunObjectives: To evaluate the performance of convolutional neural network (CNN) architectures to distinguish eyes with glaucoma from normal eyes. Materials and Methods: A total of 9,950 fundus photographs of 5,388 patients from the database of Eskisehir Osmangazi University Faculty of Medicine Ophthalmology Clinic were labelled as glaucoma, glaucoma suspect, or normal by three different experienced ophthalmologists. The categorized fundus photographs were evaluated using a state-of-the-art two-dimensional CNN and compared with deep residual networks (ResNet) and very deep neural networks (VGG). The accuracy, sensitivity, and specificity of glaucoma detection with the different algorithms were evaluated using a dataset of 238 normal and 320 glaucomatous fundus photographs. For the detection of suspected glaucoma, ResNet-101 architectures were tested with a data set of 170 normal, 170 glaucoma, and 167 glaucoma-suspect fundus photographs. Results: Accuracy, sensitivity, and specificity in detecting glaucoma were 96.2%, 99.5%, and 93.7% with ResNet-50; 97.4 degrees A, 97.8%, and 97.1% with ResNet-101; 98.9%, 100%, and 98.1% with VGG-19, and 99.4%, 100%, and 99% with the 2D CNN, respectively. Accuracy, sensitivity, and specificity values in distinguishing glaucoma suspects from normal eyes were 62%, 68%, and 56% and those for differentiating glaucoma from suspected glaucoma were 92%, 81%, and 97%, respectively. While 55 photographs could be evaluated in 2 seconds with CNN, a clinician spent an average of 24.2 seconds to evaluate a single photograph. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs.Article Citation - WoS: 11Citation - Scopus: 14Evaluation of Mother Wavelets on Steady-State Visually-Evoked Potentials for Triple-Command Brain-Computer Interfaces(Tubitak Scientific & Technical Research Council Turkey, 2021-09-23) Sayilgan, Ebru; Yuce, Yilmaz Kemal; Isler, YalcinWavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals; however, there exists no evaluation for steady-state visually-evoked potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore, if any, the mother wavelet that suits best to represent SSVEP signals for classification purposes in BCIs. In this study, three common wavelet-based features (variance, energy, and entropy) extracted from SSVEP signals for five distinct EEG frequency bands (delta, theta, alpha, beta, and gamma) were classified to determine three different user commands using six fundamental classifier algorithms. The study was repeated for six different commonly-used mother wavelet functions (haar, daubechies, symlet, coiflet, biorthogonal, and reverse biorthogonal). The best discrimination was obtained with an accuracy of 100% and the average of 75.85%. Besides, ensemble learner gives the highest accuracies for half of the trials. Haar wavelet had the best performance in representing SSVEP signals among other all mother wavelets adopted in this study. Concomitantly, all three features of energy, variance, and entropy should be used together since none of these features had superior classifier performance alone.