Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0).Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances.Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes,CA7, SPIB, GUCA2B, AQP8, IL6RandCWH43;oncogenes,SPP1andTCN1. Genes of higher significances showed lower variation in rank ordering by different methods.Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes,CA7,TCN1, andCWH43have not been reported to be related to CRC.

The current shortage of nurses is an important global issue. Most male nurses leave nursing within four years of starting their nursing career. It is crucial to understand the influencing factors on newly graduated male nurses staying in nursing. Previous studies on intentions to stay as nurses were seldom based on theory and failed to consider the differences between genders. Based on the Job Demands-Resources (JD-R) model, this study tested the model that social support, resilience, and nursing professional commitment influence the intention to stay and the mediating effect of nursing professional commitment in the above relationship. This cross-sectional study adopted purposive and snowball sampling methods. Data were collected using online questionnaire, and 272 newly graduated male nurses completed it. The hypothetical model had a good fit with the data. Nursing professional commitment had a complete mediating effect between social support and intention to stay and between resilience and intention to stay. Nursing professional commitment was highly positively correlated to intention to stay. It is suggested that future research and practice should enhance male nurses’ professional commitment to increase their intention to stay. The findings can serve as reference for developing newly graduated male nurse retention programs.

The chain of survival has been shown to improve the chances of survival for victims of cardiac arrest. Post-cardiac arrest care has been demonstrated to significantly impact the survival of out-of-hospital cardiac arrest (OHCA). How post-cardiac arrest care influences the survival of OHCA patients has been a main concern in recent years. The objective of this study was to assess the survival outcome of hospitalized OHCA patients and determine the factors associated with improved survival in terms of survival to discharge. We conducted a retrospective observational study by analyzing records from the National Health Insurance Research Database of Taiwan from 2007 to 2013. We collected cases with an International Classification of Disease Clinical Modification, 9^th revision primary diagnosis codes of 427.41 (ventricular fibrillation, VF) or 427.5 (cardiac arrest) and excluded patients less than 18 years old, as well as cases with an unknown outcome or a combination of traumatic comorbidities. We then calculated the proportion of survival to discharge among hospitalized OHCA patients. Factors associated with the dependent variable were examined by logistic regression. Statistical analysis was conducted using SPSS 22 (IBM, Armonk, NY). Of the 11,000 cases, 2,499 patients (22.7%) survived to hospital discharge. The mean age of subjects who survived to hospital discharge and those who did not was 66.7±16.7 and 71.7±15.2 years, respectively. After adjusting for covariates, neurological failure, cardiac comorbidities, hospital level, intensive care unit beds, transfer to another hospital, and length of hospital stay were independent predictors of improved survival. Cardiac rhythm on admission was a strong factor associated with survival to discharge (VF vs. non-VF: adjusted odds ratio: 3.51; 95% confidence interval: 3.06–4.01). In conclusion, cardiac comorbidities, hospital volume, cardiac rhythm on admission, transfer to another hospital and length of hospital stay had a significant positive association with survival to discharge in hospitalized OHCA patients in Taiwan.

Background: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. Results: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… Conclusions: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.

Background: Colorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction. Objective: Microarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples. Methods: We collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues. Results: Ten genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method. Conclusions: Six genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.