Who Is Caring for the Caregiver? The Role of Cybercoping for Dementia Caregivers

The purpose of this study is to investigate the relationship between dementia caregivers’ communication behaviors (information seeking and forwarding) and their outcomes (coping outcomes: e.g., dealing better with negative feelings or improved medical outcomes). A survey data set of dementia patients’ caregivers substantiates the effects of communication behaviors about dementia illness on coping outcomes, as well as the mediating role of emotion-focused and problem-focused coping processes. Using structural equation modeling (SEM), this study found positive effects of communication behaviors on outcomes through coping processes. Further, the results indicate that communication behaviors in cyberspace are crucial for caregivers to cope with dementia, both affectively (improvement of caregivers’ emotional control) and physically (health improvement of patients). The implications for the improvement of public health through online health communication behaviors are discussed

Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data

BACKGROUND: A complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast. RESULTS: We report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs) were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis. CONCLUSION: By a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding some of the important transcriptional regulatory elements of human genes

A gene sets approach for identifying prognostic gene signatures for outcome prediction

<p>Abstract</p> <p>Background</p> <p>Gene expression profiling is a promising approach to better estimate patient prognosis; however, there are still unresolved problems, including little overlap among similarly developed gene sets and poor performance of a developed gene set in other datasets.</p> <p>Results</p> <p>We applied a gene sets approach to develop a prognostic gene set from multiple gene expression datasets. By analyzing 12 independent breast cancer gene expression datasets comprising 1,756 tissues with 2,411 pre-defined gene sets including gene ontology categories and pathways, we found many gene sets that were prognostic in most of the analyzed datasets. Those prognostic gene sets were related to biological processes such as cell cycle and proliferation and had additional prognostic values over conventional clinical parameters such as tumor grade, lymph node status, estrogen receptor (ER) status, and tumor size. We then estimated the prediction accuracy of each gene set by performing external validation using six large datasets and identified a gene set with an average prediction accuracy of 67.55%.</p> <p>Conclusion</p> <p>A gene sets approach is an effective method to develop prognostic gene sets to predict patient outcome and to understand the underlying biology of the developed gene set. Using the gene sets approach we identified many prognostic gene sets in breast cancer.</p