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.

The effect of chronic antidepressant treatment on serum brain-derived neurotrophic factor levels in depressed patients: a preliminary study.

Abstract: Recent studies suggested a role of brain-derived neurotrophic factor (BDNF) in depression. While BDNF levels are lower in depressed patients, antidepressant treatment increases serum BDNF levels of depressed patients. Our study aims to test the effect of chronic venlafaxine treatment on serum BDNF levels in patients with a major depressive disorder. Ten patients diagnosed as major depressive disorder according to DSM-IV are included in the study. Two of the patients had their first episode and were drug-naive, the other eight patients were drug-free for at least 4 weeks. The severity of depression was assessed with Hamilton Depression Rating Scale (HDRS). The control group consisted of ten age- and sex-matched subjects without any psychiatric disorder. Blood samples were collected at the baseline and after 12 weeks of antidepressant treatment (during remission). At the baseline the mean serum BDNF level was 17.9+/-9.1 ng/ml and the mean HDRS score was 23.2+/-4.6. Serum BDNF levels of the study group were significantly lower than in the control group (31.6+/-8.6 ng/ml). At the end of the study, the mean serum BDNF level was 34.6+/-7.1 ng/ml whereas the mean HDRS score was 8.2+/-3.9. From the baseline to the remission after 12 weeks of treatment, the increase in serum BDNF level and the decrease in HDRS score were statistically significant, respectively. When we compared the serum BDNF level of depressed patients at remission to that of the controls, there was no statistically significant difference. This study shows that venlafaxine treatment of depression improves serum BDNF level which may be considered as a nonspecific peripheral marker of depression.

The knowledge and attitudes of breast self-examination and mammography in a group of women in a rural area in western Turkey.

Abstract: Breast cancer appears to be a disease of both the developing and developed worlds. Among Turkish women, breast cancer is the second leading cause of cancer-related deaths. The aims of this cross-sectional study were to determine levels of knowledge about breast cancer and to evaluate health beliefs concerning the model that promotes breast self- examination (BSE) and mammography in a group of women aged 20–64 in a rural area of western Turkey. 244 women were recruited by means of cluster sampling in this study. The questionnaire consisted of sociodemographic variables, a risk factors and signs of breast cancer form and the adapted version of Champion's Health Belief Model Scale (CHBMS). Bivariate correlation analysis, Chi square test, Mann-Whitney U test and logistic regression analysis were performed throughout the data analysis. The mean age of the women was 37.7 ± 13.7. 49.2% of women were primary school graduates, 67.6% were married. Although 76.6% of the women in this study reported that they had heard or read about breast cancer, our study revealed that only 56.1% of them had sufficient knowledge of breast cancer, half of whom had acquired the information from health professionals. Level of breast cancer knowledge was the only variable significantly associated with the BSE and mammography practice (p = 0.011, p = 0.007). BSE performers among the study group were more likely to be women who exhibited higher confidence and perceived greater benefits from BSE practice, and those who perceived fewer barriers to BSE performance and possessed knowledge of breast cancer. By using the CHBMS constructs for assessment, primary health care providers can more easily understand the beliefs that influence women's BSE and mammography practice.

Antibacterial activity of some actinomycetes isolated from farming soils of Turkey

Abstract: A total of 50 different actinomycete strains were recovered from farming soil samples collected from Manisa Province and its surrounding. These were then assessed for their antibacterial activity against four phytopathogenic and six pathogenic bacteria. Results indicated that 34% of all isolates are active against, at least, one of the test organisms; Agrobacterium tumefaciens, Erwinia amylovora, Pseudomonas viridiflova, Clavibacter michiganensis subsp. michiganensis, Bacillus subtilis ATTC 6633, Klebsiella pneumoniae ATTC 10031, Enterococcus feacalis ATCC 10541, Staphylococcus aureus ATCC 6538, Esherichia coli ATCC 29998 and Sarcina lutea ATCC 9341. According to antibacterial activity and spectrum broadness, seven of the isolates were selected and characterized by conventional methods. The unusual antibiotic profile of these isolates underlined their potential as a source of novel antibiotics. Key words: Streptomyces , soil, characterization, antibacterial activity, screening. African Journal of Biotechnology Vol.3(9) 2004: 441-446

A feature selection model based on genetic rank aggregation for text sentiment classification

Abstract: Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained In order to aggregate the individual feature lists, a genetic algorithm has been utilized Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification

A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

Abstract: Typically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naive Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).