Ratlarda Deneysel Olarak Oluşturulan Asidik Deri Yanıklarında Uygulanan Ozon Tedavisinin Klinik Etkinliğinin Araştırılması*

Bu çalışmada ratlarda hidroflorik asit (HFA) ile deneysel olarak oluşturulan asidik deri yanıklarında ozon tedavisininklinik etkinliğinin araştırılması amaçlanmıştır. Çalışma materyalini 20 adet, sağlıklı erkek, 200-250 gr ağırlığındaki ratlaroluşturdu. Çalışmaya dahil edilen hayvanlar her bir grupta 10 adet hayvan olacak şekilde deney ve kontrol grubu olarakgruplandırıldı. Grupları oluşturan tüm hayvanlarda genel anestezi altında %38’lik HFA ile asidik deri yanığı oluşturuldu.Deri yanığı oluşturulan çalışma grubundaki tüm hayvanların yara bölgesine 7 gün boyunca günde bir kere ozonlanmışsıvı vazelin, kontrol grubundaki tüm hayvanlara ise %0.9’luk serum fizyolojik 7 gün süre ile günde bir kere uygulandı.Çalışmada klinik olarak değerlendirmeye alınan bül, eritem, nekroz, iyileşme, tüylenme ve oluşan hasarlı alan verilerinin istatistiksel analizleri karşılaştırıldı. İstatistiksel analiz sonucunda deney ve kontrol grupları arasında bül, eritem,nekroz, iyileşme ve oluşan hasarlı alan bakımından anlamlı farklılık olmadığı, ancak tüylenmede 5. günden itibarenanlamlı farklılık olduğu tespit edildi (P&lt;0.05). Bu çalışma sonucunda; deneysel olarak oluşturulan asidik deri yanıklarında ozon tedavisi yapılan grupta iyileşen hayvan sayısının daha fazla olduğu ve iyileşmenin 8. gün deney grubundaistatistiksel olarak anlamlılık seviyesinde olduğu tespit edildi. Ozonun asidik deri yanıklarında acil müdahale olarakuygulanan hidroterapiye göre daha iyi olabileceği fakat konuyla ilgili daha detaylı araştırmaların yapılması gerektiğikanısına varıldı.</p

Association of real-time sonoelastography findings with clinical parameters in lateral epicondylitis

The objective of this study was to investigate the role of real-time sonoelastography (RTSE) in patients with lateral epicondylitis (LE) and whether it is associated with clinical parameters. Seventeen patients with unilateral LE were enrolled in the study. The healthy elbows of the participants constituted the control group. Using B-mode ultrasound, color Doppler ultrasound, and RTSE, we prospectively examined 34 common extensor tendon elbows of 17 patients. Both color scales and strain ratio were used for evaluating RTSE images. Two radiologists evaluated the RTSE images separately. Elbow pain was scored on a 100-mm visual analog scale (VAS). Symptom duration and the presence of nocturnal pain were questioned. Quick disabilities of arm shoulder and hand (DASH) Questionnaire was applied to assess the pain, function, and disability. Nottingham health profile (NHP) was used to determine and quantify perceived health problems. Both color scales and strain ratios of the affected tendon portions were significantly different from that of healthy tendons (p < 0.001). There was no significant association between NHP, VAS, Quick DASH scores, and color scales and strain ratio. Strain ratio of the medial portion of the affected tendon was significantly correlated with symptom duration (rho = −0.61 p = 0.010) and nocturnal pain (rho = 0.522 p = 0.031). Interobserver agreement was substantial for color scales (κ = 0.74, p = 0.001) and strain ratio (ICC = 0.61, p = 0.031). RTSE may facilitate differentiation between healthy and affected elbows as a feasible and practical supplementary method with substantial interobserver agreement. RTSE was superior to B-mode ultrasound and color Doppler ultrasound in discriminating tendons with LE. Strain ratio of the medial portion of the tendon is associated moderately with nocturnal pain and symptom duration. No other associations were present between RTSE findings and clinical or functional parameters. © 2015, Springer-Verlag Berlin Heidelberg

Wheat flour milling yield estimation based on wheat kernel physical properties using artificial neural networks

Wheat is a basicfood raw material for the majority of people around the world as wheat-based products provide an important part of the daily energy intake in many countries. Wheat is generally milled into flour prior to use in the bakery industry. Flour yield is one of the major quality criteria in wheat milling. Flour yield determination requires large amounts of samples, costly machines, grinding applications that require a long working time and a considerable amount of workload.In this study, Artificial Neural Network(ANN) approach has been employed to predict flour milling yield. The ANN was designed in the Matlabusing such wheat physical properties as hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness. Flour yields and four different kernel physical features (hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness) were first collected from 2400 wheat samples through the conventional methods. The ANN was trained using 85% of 2400 yield data and tested with the remaining 15% data. In the training of the ANN, various models have been investigated to find the best ANN structure. Additionally, two datasets with and without grain hardness have been employed to determine the effect of grain hardness on the prediction performance of the ANN model. It was found that grain hardnesswhichreduced the MAE values from 2.3333 to 2.2611 and RMSE values from 3.0775 to 2.9146gave better result. The results proved that the developed ANN model can be used to estimateflour yield using wheat physical properties

CNN–SVM hybrid model for varietal classification of wheat based on bulk samples

WOS:000794076800001Determining the variety of wheat is important to know the physical and chemical properties which may be useful in grain processing. It also affects the price of wheat in the food industry. In this study, a Convolutional Neural Network (CNN)-based model was proposed to determine wheat varieties. Images of four different piles of wheat, two of which were the bread and the remaining durum wheat, were taken and image pre-processing techniques were applied. Small-sized images were cropped from high-resolution images, followed by data augmentation. Then, deep features were extracted from the obtained images using pre-trained seven different CNN models (AlexNet, ResNet18, ResNet50, ResNet101, Inceptionv3, DenseNet201, and Inceptionresnetv2). Support Vector Machines (SVM) classifier was used to classify deep features. The classification accuracies obtained by classification with various kernel functions such as Linear, Quadratic, Cubic and Gaussian were compared. The highest wheat classification accuracy was achieved with the deep features extracted with the Densenet201 model. In the classification made with the Cubic kernel function of SVM, the accuracy value was 98.1%

Surgical site infection rates in 16 cities in Turkey: findings of the International Nosocomial Infection Control Consortium (INICC)

Conclusions: In most of the 22 types of SP analyzed, our SSI rates were higher than the CDC NHSN rates and similar to the INICC rates. This study advances the knowledge of SSI epidemiology in Turkey, allowing the implementation of targeted interventions. Copyright (C) 2015 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved