Browsing by Author "Borcek, Alp Ozgun"

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Effects of Quercetin on Chronic Constriction Nerve Injury in an Experimental Rat Model
(2016) Civi, Soner; Emmez, Gokcen; Dere, Umit Akin; Borcek, Alp Ozgun; Emmez, Hakan; https://orcid.org/0000-0002-1055-5152; 26960544; U-2400-2018
Flavonoids are popular substances in the literature, with proven effects on cardiovascular, neoplastic and neurodegenerative diseases. Antioxidant effect is the most pronounced and studied one. Among thousands of flavonoids, quercetin (QUE) is a prototype with significant antioxidant effects. This study aims to demonstrate the effects of QUE in an experimental rat model of chronic constriction injury (CCI). A two-level study was designed with 42 adult Wistar rats that were randomly assigned to different groups. In the first part, animals in sham, control, quercetin, morphine and gabapentine groups received chronic constriction injury to their sciatic nerves and received a single dose of QUE, morphine and gabapentine. In the second part, different dose regimens of QUE were administered to different groups of animals. Pre-injury and post-injury assessments for mechanical hypersensitivity, thermal sensitivity, locomotor activity and anxiety were recorded and statistical comparisons were performed between different groups. Comparison of QUE with morphine and gabapentine has revealed significant effects of this agent in the current chronic constriction injury model. QUE was significantly superior to Gabapentine and morphine in terms of alleviating mechanical and thermal hypersensitivity. Additionally, pre-injury administration of QUE for 4 days demonstrated long-term effectiveness on mechanical hypersensitivity. This preliminary report the on effects of QUE in a chronic constriction injury model proved significant effects of the agent, which should be supplemented with different studies using different dose regimens.
A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study
(2020) Atici, Mehmet Ali; Sagiroglu, Seref; Celtikci, Pinar; Ucar, Murat; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 0000-0002-1655-6957; 31608975
AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS: A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration. In order to increase data processing time, every single image converted into a Jpeg image which has a resolution of 320x320. Accuracy, precision and recall rates were calculated after training of the algorithm. RESULTS: Following training, CNN achieved acceptable performance ratios of 0.854 to 0.944 for accuracy, 0.812 to 0.980 for precision and 0.738 to 0.907 for recall. Also, CNN was able to detect HGG cases even though there is no apparent mass lesion in the given image. CONCLUSION: Our preliminary findings demonstrate; currently proposed CNN model achieves acceptable performance results for the automatic detection of HGGs on T2-weighted images.
Pathological Evaluation of the Filum Terminale Tissue After Surgical Excision
(2015) Durdag, Emre; Borcek, Pelin Bayik; Ocal, Ozgur; Borcek, Alp Ozgun; Emmez, Hakan; Baykaner, M. Kemali; 0000-0001-6939-5491; 0000-0002-6222-382X; 25712741; AAK-1734-2021; O-6840-2017
Tethered cord syndrome (TCS) is an important disease and can produce progressive neurological symptoms. Studies about the filum terminale (FT) have drawn attention to the importance of histopathological investigation of this structure. The most interesting of these subtypes is the FT that incorporates peripheral nerve fibers (PNF). Our study aimed to analyze the frequency of PNF in the FT of 40 cases diagnosed with TCS. We performed a retrospective histopathological investigation of FT excised during surgery of patients with TCS who underwent de-tethering. Neurologic and other types of postoperative complications were also revised. Analysis of the samples showed six dominant histopathological subtypes in the FT: fibroadipose tissues including peripheral nerve bundles (n = 14, 37 %), fibroadipose tissue (n = 10, 25 %), fibrous or adipose tissue (n = 7, 17 %), glial tissues including peripheral nerve sections (n = 4, 10 %), and ependymal and glial tissues (n = 4, 10 %). None of the patients presented with neurologic postoperative complications. Embryologic studies revealed that it is common to encounter different histological subtypes of FT pathology. However, the presence of peripheral nerve cells in the FT is important for neurosurgical practice due to the risk of sectioning a functional structure during surgery. In our analysis, we demonstrated the high frequency of PNF in FT pathology. However, since none of the patients showed any symptoms of neurologic deterioration, we considered that these fibers were probably not functional. Our findings emphasize the importance of neuromonitoring in TCS surgery. Although we consider that most of the fibers are probably not functional, neuromonitoring after surgery may prevent serious complications.
Spine Tango in Turkish: Development of a Local Registry System
(2017) Civi, Soner; Borcek, Alp Ozgun; Bulduk, Erkut B.; Emmez, Hakan; Kaymaz, Memduh; 0000-0002-1055-5152; 27593753; U-2400-2018
AIM: Successfully established registry systems, rather than personal efforts to collect data, are required to record, analyze, compare and secure patient related data. Unfortunately, our country does not have such patient registry systems for spinal pathologies and surgeries at this time. In order to fill this gap in patient management in Turkey, the authors adopted already established Spine Tango registry system in a unique way answering the requirements of our health system. This article aims to present the adaptation process of Spine Tango forms for use in Turkish and describe the first implementation with 50 patients treated for spinal pathologies in a tertiary referral center. MATERIAL and METHODS: In 2011, an effort was initiated by the first author to translate the original Spine Tango forms into Turkish. Funding for this project was provided by authors themselves. With the assistance of a Spine Tango team, the translation process was completed. The Turkish forms were then used in an academic institution with a high spinal workload. A local solution was developed by the authors using commercially available software and mobile instruments. This system was tested with 50 spine patients from June 2012 to January 2013. RESULTS: The analysis of the data gathered using the new Turkey Spine Tango registry system was successful. CONCLUSION: In an environment of exponentially increasing medical data, successfully established registry systems have the potential to facilitate patient management. The authors recommend the use of Turkish Spine Tango forms for clinics performing spinal interventions.
Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study
(2022) Aydogan Duman, Ebru; Sagiroglu, Seref; Celtikci, Pinar; Demirezen, Mustafa Umut; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 34542897
AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time. MATERIAL and METHODS: MRIs of 300 healthy subjects were employed as training set. Additionally, test data were consisting anonymized T2-weigted MRIs of 27 healthy subjects and 27 HGG patients. Consecutive axial T2-weigted MRI slices of every subject were extracted and resized to 364x448 pixel resolution. The generative model produced random normal synthetic images and used these images for calculating residual loss to measure visual similarity between input MRIs and generated MRIs. RESULTS: The model correctly detected anomalies on 24 of 27 HGG patients' MRIs and marked them as abnormal. Besides, 25 of 27 healthy subjects' MRIs in the test dataset detected correctly as healthy MRI. The accuracy, precision, recall, and AUC were 0.907, 0.892, 0.923, and 0.907, respectively. CONCLUSION: Our proposed model demonstrates acceptable results can be achieved only by training with normal subject MRIs via using DCGAN model. This model is unique because it learns only from normal MRIs and it is able to find any abnormality which is different than the normal pattern.