[Effects of humor therapy on fatigue and depression of multiple sclerosis [MS] patients]

Multiple sclerosis [MS] is the most common disabling condition in young adults, which is caused by an inflammatory demyelination process in central nervous system. Fatigue and depression are the primary symptoms leading to dysfunction as well as disability in activities of daily living and decreased quality of life. Because of many drug-associated complications, applying other methods to lessen the symptoms seems reasonable. The aim of this study was to determine the effects of humor on fatigue and depression of clients referring to Iranian MS Society. In this one-group before-after clinical trial, 30 MS clients were selected by convenience sampling method. A 4-part questionnaire including demographics, items related to the condition, Fatigue Severity Scale [FSS] and Beck's Depression Inventory was used for data collection, validated and made reliable by content and test-retest methods respectively. The clients took part in humor therapy sessions 3 times a week, each lasting 30 minutes for 12 weeks. The sessions were hold during the day with entertaining and funny programs recorded on compact discs [CDs]. The clients completed the questionnaire before and after the intervention. Data were then analyzed by different statistical methods. A significant decrease was found in mean severities of fatigue and depression after the intervention [P<0.01]. Therefore, the hypothesis of the study denoting the effects of humor therapy on severity of fatigue and depression in clients with MS was verified. The study revealed that humor therapy may decrease the fatigue and depression of clients with MS. Humor as a simple, low-cost and noninvasive method can be used to overcome many problems of these clients and ultimately lead to decreased fatigue and depression

[Fuzzy clustering approach in DNAmicroarray]

Microarray techniques are successfully used to investigate thousands of gene expression profiling in a variety of genomic analyses such as gene identification, drug discovery and clinical diagnosis, providing a large amount of genomic data for the overall research community. Statistical analysis of such databases included normalization, clustering, classification, etc. The present study surveyed the application of fuzzy clustering technique in DNA microarray analysis. Golub, et al collected data bases of leukemia based on the method of oligonucleotide in 1999. The data are on the internet for free. In this paper we did analysis on this data set and gene expression data were clustered by fuzzy clustering. Data set included 20 Acute Lymphoblastic Leukemia [ALL] patients and 14 Acute Myeloid Leukemia [AML] patients. Efficiency of clustering was compared with regard to real grouping [ALL and AML]. We used R software for data analysis Specificity and sensitivity of fuzzy clustering in diagnosing of ALL patients are 90% and 93%, respectively. These results show a good accomplishment of both clustering methods. It is considerable that, due to clustering methods results, one of the samples was placed in ALL group, which had been in AML group in clinical test. With regard to concordance of the results with real grouping of data, it could be said that we can use these methods in cases where we don't have accurate information of real data grouping. Moreover, results of clustering might distinguish subgroups of data in such a way

[Gene expression data clustering and it's application in differential analysis of leukemia]

DNA microarray technique is one of the most important categories in bioinformatics, which allows the possibility of monitoring thousands of expressed genes has been resulted in creating giant data bases of gene expression data, recently. Statistical analysis of such databases included normalization, clustering, classification and etc. Golub et al [1999] collected data bases of leukemia based on the method of oligonucleotide. The data is on the internet. In this paper, we analyzed gene expression data. It was clustered by several methods including multi-dimensional scaling, hierarchical and non-hierarchical clustering. Data set included 20 Acute Lymphoblastic Leukemia [ALL] patients and 14 Acute Myeloid Leukemia [AML] patients. The results of tow methods of clustering were compared with regard to real grouping [ALL and AML]. R software was used for data analysis. Specificity and sensitivity of divisive hierarchical clustering in diagnosing of ALL patients were 75% and 92%, respectively. Specificity and sensitivity of partitioning around medoids in diagnosing of ALL patients were 90% and 93%, respectively. These results showed a well accomplishment of both methods of clustering. It is considerable that, due to clustering methods results, one of the samples was placed in ALL groups, which was in AML group in clinical test. With regard to concordance of the results with real grouping of data, therefore we can use these methods in the cases where we don't have accurate information of real grouping of data. Moreover, Results of clustering might distinct subgroups of data in such a way that would be necessary for concordance with clinical outcomes, laboratory results and so on

[Clustering approach in DNA microarray analysis]

Microarray DNA technology has paved the way for investigators to expressed thousands of genes in a short time. Analysis of this big amount of raw data includes normalization, clustering and classification. The present study surveys the application of clustering technique in microarray DNA analysis. We analyzed data of Van't Veer et al study dealing with BRCA1 and BRCA2 mutations in breast cancer. It was consisted of 18 patients with BRCA1 and 2 patients with BRCA2 mutation. Gene expression data were clustered using hierarchical and non-hierarchical approach. Then different clustering approaches were compared according to the actual classification with R software. Hierarchical clustering showed a sensitivity of 94% and specificity of 100% in detecting BRCA1 gene. These figures were 89% and 100% for non-hierarchical clustering, respectively, indicating a satisfactory performance for both approaches. All clustering approaches classified sample No. 95 in BRCA2 group, however, clinical manifestations put her in BRCA1 group. With respect to satisfactory coincidence between clustering and actual classification results, clustering approach could be applied for cases when actual classification is missing