Background: In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs).

This study aimed at examining the spectrum and frequency of symptoms of ecstasy dependence and their correlation with psychopathology by controlling polysubstance use in Taiwanese adolescents. Two hundred adolescents who had used ecstasy were recruited into this study. Symptoms of ecstasy dependence that had occurred in the preceding year were determined by an interview using the Kiddie epidemiologic version of the Schedule for Affective Disorders and Schizophrenia. The adolescents' psychopathology was examined using the Symptom Checklist-90-Revised Scale. The proportion of participants who had symptoms of ecstasy dependence was calculated. The association between the number of symptoms of ecstasy dependence and psychopathology was examined by using stepwise multiple regression analysis. The results indicated that "continuing ecstasy use despite knowledge of having a problem related to ecstasy use," "spending a great deal of time in activities related to ecstasy use or to recover from its effects," and "ecstasy use tolerance" were the 3 most prevalent symptoms of dependence, and "withdrawal" was the symptom least reported. Heavy ecstasy use led to more symptoms of ecstasy dependence than light use. Symptoms of ecstasy dependence independently increased the risk of severe psychopathology after controlling the effects of polysubstance use. The results of this study indicated that adolescents were aware of the adverse effects of ecstasy use and that repeated ecstasy use would result in dependence on it. Screening the dependence symptoms of adolescent ecstasy users may help clinicians more thoroughly understand their psychopathology.

The Pittsburgh Sleep Quality Index (PSQI) is an effective instrument for measuring the quality of sleep in older adults. In this study, we used Rasch analysis to validate the items of the revised PSQI (SC_PSQI) that contribute to a single construct. A total of 3,742 workers agreed to participate in this study. Both the appropriateness of the scoring rubrics and the unidimensionality of the SC_PSQI scale were investigated. All nine items fit the model's expectations rather well. These results indicate that the SC_PSQI with a 0 to 2 scoring scale can be used as a unidimensionality to assess sleep quality.

BackgroundFor hospital accreditation and health promotion reasons, we examined whether the 22-item Job Content Questionnaire (JCQ) could be applied to evaluate job strain of individual hospital employees and to determine the number of factors extracted from JCQ. Additionally, we developed an Excel module of self-evaluation diagnostic system for consultation with experts.MethodsTo develop an Excel-based self-evaluation diagnostic system for consultation to experts to make job strain assessment easier and quicker than ever, Rasch rating scale model was used to analyze data from 1,644 hospital employees who enrolled in 2008 for a job strain survey. We determined whether the 22-item Job Content Questionnaire (JCQ) could evaluate job strain of individual employees in work sites. The respective item responding to specific groups' occupational hazards causing job stress was investigated by using skewness coefficient with its 95% CI through item-by-item analyses.ResultsEach of those 22 items on the questionnaire was examined to have five factors. The prevalence rate of Chinese hospital workers with high job strain was 16.5%.ConclusionsGraphical representations of four quadrants, item-by-item bar chart plots and skewness 95% CI comparison generated in Excel can help employers and consultants of an organization focusing on a small number of key areas of concern for each worker in job strain.

We demonstrated that our Bayesian based approach is a promising method to assess the gene-gene and gene-environment interactions in chronic fatigue syndrome patients by using genetic factors, such as SNPs, and demographic factors such as age, gender and BMI.