Elektrik Ve Enerji Bölümü
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Öğe Ameliorating effect of hawthorn (Crataegus oxyacantha) and physical exercise on acute penicillin induced seizures in gerbils(African Networks Ethnomedicines, 2016) Çakır, Serkan; Orallar, Hayriye; Çetinkaya, Ayhan; Kayacan, Yıldırım; Önal, Ali Can; Yıldırım, Arzu; Benek, Selim; Özkan, Murat; Okur, NezihBackground: The aim of the present study was to assess the effect of Hawthorn (Crataegus oxyacantha) and physical activity. We studied its effect on penicillin induced epilepsy. in gerbils. Materal and Methods: Epilepsy was induced by administration of peniciline G (500 IU, ip). The gerbils were divided randomly in four groups (6 animals per each group) and studied as described below: 1) Control group 2) Exercise group (30 min/each day for 8 weeks) (Eg) 3) Extract group, 50mg/kg/day/animal in 1 ml saline, 3 h prior to exercise (Exe) 4) Exercise+ Extract + (Exe+ Ex). The severity of epilepsy was observed and recorded. Results: The means of latencies (Mean +/- SE) were 236 +/- 45, 369 +/- 36, 386 +/- 58 and 433 +/- 37 ms in groups of control, Exe, Ex, and Exe+ Ex respectively. The mean spike latency significantly (P= 0,033 F= 3,560) decreased in Exe, Ex and Exe+ Ex when compared control. Although spike frequency significantly (P< 0.05) diminished in groups of Exe and Ex, no significant decrease was observed in control and Exe+ Ex. Similar trend was seen for amplitude values. Spike amplitude values were determined to be significantly (P< 0.05) lower than those of control and Exe+ Ex. Conclusion: Crataegus oxyacantha extract has shown positive affect to ameliorate on some seizure parameters in this study. However, further more advanced physiologic and neurochemical studies are required to determine the mechanisms involved.Öğe Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images(Wolters Kluwer Medknow Publications, 2016) Özkan, Murat; Çakıroğlu, Murat; Kocaman, Orhan; Kurt, Mevlüt; Yılmaz, Bülent; Can, Güray; Korkmaz, UğurAim: The aim was to develop a high-performance computer-aided diagnosis (CAD) system with image processing and pattern recognition in diagnosing pancreatic cancer by using endosonography images. Materials and Methods: On the images, regions of interest (ROI) of three groups of patients (<40, 40-60 and >60) were extracted by experts; features were obtained from images using three different techniques and were trained separately for each age group with an Artificial Neural Network (ANN) to diagnose cancer. The study was conducted on endosonography images of 202 patients with pancreatic cancer and 130 noncancer patients. Results: 122 features were identified from the 332 endosonography images obtained in the study, and the 20 most appropriate features were selected by using the relief method. Images classified under three age groups (in years; <40, 40-60 and >60) were tested via 200 random tests and the following ratios were obtained in the classification: accuracy: 92%, 88.5%, and 91.7%, respectively; sensitivity: 87.5%, 85.7%, and 93.3%, respectively; and specificity: 94.1%, 91.7%, and 88.9%, respectively. When all the age groups were assessed together, the following values were obtained: accuracy: 87.5%, sensitivity: 83.3%, and specificity: 93.3%. Conclusions: It was observed that the CAD system developed in the study performed better in diagnosing pancreatic cancer images based on classification by patient age compared to diagnosis without classification. Therefore, it is imperative to take patient age into consideration to ensure higher performance.
