ObjectiveThe impact of schizophrenia on vital diseases, such as chronic kidney disease (CKD), has not as yet been verified. This study aims to establish whether there is an association between schizophrenia and CKD.DesignA nationwide matched cohort study.SettingTaiwan's National Health Insurance Research Database.ParticipantsA total of 2338 patients with schizophrenia, and 7014 controls without schizophrenia (1:3), matched cohort for sex, age group, geography, urbanisation and monthly income, between 1 January 2003 and 31 December 2007, based on the International Classifications of Disease Ninth Edition (ICD-9), Clinical Modification codes.Primary and secondary outcome measuresAfter making adjustments for confounding risk factors, a Cox proportional hazards model was used to compare the risk of developing CKD during a 3-year follow-up period from the index date.ResultsOf the 2338-subject case cohort, 163 (6.97%) developed a CKD, as did 365 (5.20%) of the 7014 control participants. Cox proportional hazards regression analysis revealed that patients with schizophrenia were more likely to develop CKD (HR=1.36, 95% CI 1.13 to 1.63; p<0.001). After adjusting for gender, age group, hypertension, diabetes mellitus, hyperlipidaemia, heart disease and non-steroid anti-inflammatory drugs (NSAIDs) usage, the HR for patients with schizophrenia was 1.25 (95% CI 1.04 to 1.50; p<0.05). Neither typical nor atypical antipsychotics was associated an increased risk of CKD in patients with schizophrenia.ConclusionsThe findings from this population-based retrospective cohort study suggest that schizophrenia is associated with a 25% increase in the risk of developing CKD within only a 3-year follow-up period.

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BackgroundFew studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial–mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients’ survival.ResultsThis work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies.ConclusionsThe proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0463-x) contains supplementary material, which is available to authorized users.

With the development of electronics in governments and business, the implementation of these services are increasing demand for servers. Continued expansion of servers represents our need for more space, power, air conditioning, network, human resources and other infrastructure. Regardless of how powerful servers now become, we do not make good use of all resources and strive for the waste. In this paper, the Green Power Management (GPM) is proposed for load balancing for virtual machine management on cloud. It includes three main phrases: (1) supporting green power mechanism, (2) implementing virtual machine resource monitor onto OpenNebula with web-based interface, and (3) integrating a Dynamic Resource Allocation (DRA) and OpenNebula functions as bases instead of traditionally booting physical machines with command mode.