Celal Bayar University

About: Celal Bayar University is a education organization based out in Magnesia ad Sipylum, Turkey. It is known for research contribution in the topics: Population & Heat transfer. The organization has 2960 authors who have published 6024 publications receiving 100646 citations.

Extraction and properties of Ferula communis (chakshir) fibers as novel reinforcement for composites materials

Abstract: The aim of this study is to examine the use of Ferula communis fibers as potential reinforcement in polymer composites The fibers are extracted from the F communis plant which grows in Selcuk, Izmir in western Turkey The chemical composition of ferula fibers in terms of cellulose, lignin, and ash contents was determined Surface functional groups of ferula fibers were obtained by fourier transform infrared and X-ray photoelectron spectroscopy Crystallinity index and crystallite size were determined by X-ray diffraction analysis The morphology of ferula fibers was investigated through scanning electron microscopy, the thermal behavior through thermogravimetric and differential scanning calorimetry analyses The real density of ferula fibers was measured by means of Archimedes method with ethanol The mechanical properties of F communis were measured through single fiber tensile tests The interfacial shear strength (IFSS) in a polyester matrix has been estimated from the pull-out test

Awareness of breast cancer risk factors and practice of breast self examination among high school students in Turkey

Abstract: Young breast cancer patients have a lower rate of survival than old breast cancer patients due to being diagnosed at advanced stages. Breast self-examination makes women more "breast aware", which in turn may lead to an earlier diagnosis of breast cancer. The purpose of this study was to investigate knowledge and practice of breast self-examination and to determine knowledge of risk factors for breast cancer among high school students. This is a descriptive and cross-sectional study. It was conducted in a high school in Manisa, Turkey. The study sample included 718 female high school students. A socio-demographic characteristics data form, knowledge of breast self examination and risk factors for breast cancer form and breast self examination practice form were used to collect data. The female high school students had insufficient knowledge about breast self-examination and a low percentage of students reported that they had performed breast self examination monthly. The most common reason for not doing breast self- examination was "not knowing how to perform breast self-examination" (98.5%). Most of the students had little knowledge of the risk factors for breast cancer. The most widely known risk factor by the students was personal history of breast cancer (68.7%). There was a significant relation between breast self-examination practice and age, school grade, knowledge about breast cancer and knowledge about breast self- examination. There is a need to increase knowledge of adolescent females about the risks of breast cancer and benefits of early detection. In fact, health care professionals can develop effective breast health care programs and help young women to acquire good health habits.

A Review for Solitary Plasmacytoma of Bone and Extramedullary Plasmacytoma

Abstract: Solitary plasmacytoma (SP) is characterized by a mass of neoplastic monoclonal plasma cells in either bone (SBP) or soft tissue without evidence of systemic disease attributing to myeloma. Biopsy confirmation of a monoclonal plasma cell infiltration from a single site is required for diagnosis. The common presentation of SBP is in the axial skeleton, whereas the extramedullary plasmacytoma (EMP) is usually seen in the head and neck. The ratio of SP seen at males to females is 2 : 1 and the median age of patients is 55 years. The incidence rate of SP in black race is approximately 30% higher than the white race. Incidence rate increases exponentially by advancing age. SBP has a significant higher risk for progression to myeloma, and the choice of treatment is radiotherapy (RT) that is applied with curative intent at min. 4000 cGy. By only RT application, long-term disease-free survival (DFS) is possible for approximately 30% of patients with SBP and 65% of patients with EMP.

Antibacterial activities of some marine algae from the Aegean Sea (Turkey)

Abstract: In this study, methanolic extracts of six marine algae belong to Rhodophyceae (Corallina officinalis), Phaeophyceae (Cystoseira barbata, Dictyota dichotoma,Halopteris filicina, Cladostephus spongiosus f. verticillatus) and Chlorophyceae (Ulva rigida) from the North Aegean Sea (Turkey) were studied for their antibacterial activity against pathogenic microbes, 3 gram positive (Staphylococcus aureus, Micrococcus luteus and Enterococcus faecalis) and 3 Gram negative (Escherichia coli, Enterobacter aerogenes and E. coli O157:H7) in vitro. Extracts of all the test marine algae except C. officinalis showed inhibition against S. aureus. On the other hand, highest inhibiton activity among all the extratcs was shown to E . aerogenes by C . officinalis. The extract from C. barbata has shown broader activity spectrum against all the test organisms. Key words: Aegean sea, antib In this study, methanolic extracts of six marine algae belong to Rhodophyceae (Corallina officinalis), Phaeophyceae (Cystoseira barbata, Dictyota dichotoma,Halopteris filicina, Cladostephus spongiosus f. verticillatus) and Chlorophyceae (Ulva rigida) from the North Aegean Sea (Turkey) were studied for their antibacterial activity against pathogenic microbes, 3 gram positive (Staphylococcus aureus, Micrococcus luteus and Enterococcus faecalis) and 3 Gram negative (Escherichia coli, Enterobacter aerogenes and E. coli O157:H7) in vitro. Extracts of all the test marine algae except C. officinalis showed inhibition against S. aureus. On the other hand, highest inhibiton activity among all the extratcs was shown to E . aerogenes by C . officinalis. The extract from C. barbata has shown broader activity spectrum against all the test organisms. Key words: Aegean sea, antibacter In this study, methanolic extracts of six marine algae belong to Rhodophyceae (Corallina officinalis), Phaeophyceae (Cystoseira barbata, Dictyota dichotoma,Halopteris filicina, Cladostephus spongiosus f. verticillatus) and Chlorophyceae (Ulva rigida) from the North Aegean Sea (Turkey) were studied for their antibacterial activity against pathogenic microbes, 3 gram positive (Staphylococcus aureus, Micrococcus luteus and Enterococcus faecalis) and 3 Gram negative (Escherichia coli, Enterobacter aerogenes and E. coli O157:H7) in vitro. Extracts of all the test marine algae except C. officinalis showed inhibition against S. aureus. On the other hand, highest inhibiton activity among all the extratcs was shown to E . aerogenes by C . officinalis. The extract from C. barbata has shown broader activity spectrum against all the test organisms. Key words: Aegean sea, antibacterial activity, Corallina officinalis, marine algae. ial activity, Corallina officinalis, marine algae. acterial activity, Corallina officinalis, marine algae.

A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer

Abstract: A novel classification model based on fuzzy-rough nearest neighbor method.Fuzzy-rough instance selection.Consistency-based subset evaluation combined with re-ranking algorithm.The automated diagnosis of breast cancer with a classification accuracy of 99.71%. Breast cancer is one of the most common and deadly cancer for women. Early diagnosis and treatment of breast cancer can enhance the outcome of the patients. The development of classification models with high accuracy is an essential task in medical informatics. Machine learning algorithms have been widely employed to build robust and efficient classification models. In this paper, we present a hybrid intelligent classification model for breast cancer diagnosis. The proposed classification model consists of three phases: instance selection, feature selection and classification. In instance selection, the fuzzy-rough instance selection method based on weak gamma evaluator is utilized to remove useless or erroneous instances. In feature selection, the consistency-based feature selection method is used in conjunction with a re-ranking algorithm, owing to its efficiency in searching the possible enumerations in the search space. In the classification phase of the model, the fuzzy-rough nearest neighbor algorithm is utilized. Since this classifier does not require the optimal value for K neighbors and has richer class confidence values, this approach is utilized for the classification task. To test the efficacy of the proposed classification model we used the Wisconsin Breast Cancer Dataset (WBCD). The performance is evaluated using classification accuracy, sensitivity, specificity, F-measure, area under curve, and Kappa statistics. The obtained classification accuracy of 99.7151% is a very promising result compared to the existing works in this area reporting the results for the same data set.