BackgroundCategorizing protein-encoding transcriptomes of normal tissues into housekeeping genes and tissue-selective genes is a fundamental step toward studies of genetic functions and genetic associations to tissue-specific diseases. Previous studies have been mainly based on a few data sets with limited samples in each tissue, which restrained the representativeness of their identified genes, and resulted in low consensus among them.ResultsThis study compiled 1,431 samples in 43 normal human tissues from 104 microarray data sets. We developed a new method to improve gene expression assessment, and showed that more than ten samples are needed to robustly identify the protein-encoding transcriptome of a tissue. We identified 2,064 housekeeping genes and 2,293 tissue-selective genes, and analyzed gene lists by functional enrichment analysis. The housekeeping genes are mainly involved in fundamental cellular functions, and the tissue-selective genes are strikingly related to functions and diseases corresponding to tissue-origin. We also compared agreements and related functions among our housekeeping genes and those of previous studies, and pointed out some reasons for the low consensuses.ConclusionsThe results indicate that sufficient samples have improved the identification of protein-encoding transcriptome of a tissue. Comprehensive meta-analysis has proved the high quality of our identified HK and TS genes. These results could offer a useful resource for future research on functional and genomic features of HK and TS genes.

BackgroundThe accuracy of quantitative real-time PCR (qRT-PCR) is highly dependent on reliable reference gene(s). Some housekeeping genes which are commonly used for normalization are widely recognized as inappropriate in many experimental conditions. This study aimed to identify reference genes for clinical studies through microarray meta-analysis of human clinical samples.Methodology/Principal FindingsAfter uniform data preprocessing and data quality control, 4,804 Affymetrix HU-133A arrays performed by clinical samples were classified into four physiological states with 13 organ/tissue types. We identified a list of reference genes for each organ/tissue types which exhibited stable expression across physiological states. Furthermore, 102 genes identified as reference gene candidates in multiple organ/tissue types were selected for further analysis. These genes have been frequently identified as housekeeping genes in previous studies, and approximately 71% of them fall into Gene Expression (GO:0010467) category in Gene Ontology.Conclusions/SignificanceBased on microarray meta-analysis of human clinical sample arrays, we identified sets of reference gene candidates for various organ/tissue types and then examined the functions of these genes. Additionally, we found that many of the reference genes are functionally related to transcription, RNA processing and translation. According to our results, researchers could select single or multiple reference gene(s) for normalization of qRT-PCR in clinical studies.

Au-Pd core-shell nanocrystals with cubic, truncated cubic, cuboctahedral, truncated octahedral, and octahedral structures have been employed to form micrometer-sized polyhedral supercrystals by both the droplet evaporation method and novel surfactant diffusion methods. Observation of cross-sectional samples indicates shape preservation of interior nanocrystals within a supercrystal. Low-angle X-ray diffraction techniques and electron microscopy have been used to confirm the presence of surfactant between contacting nanocrystals. By diluting the nanocrystal concentration or increasing the solution temperature, supercrystal size can be tuned gradually to well below 1 μm using the surfactant diffusion method. Rectangular supercrystal microbars were obtained by increasing the amounts of cubic nanocrystals and surfactant used. Au-Ag core-shell cubes and PbS cubes with sizes of 30-40 nm have also been fabricated into supercrystals, showing the generality of the surfactant diffusion approach to form supercrystals with diverse composition. Electrical conductivity measurements on single Au-Pd supercrystals reveal loss of metallic conductivity due to the presence of insulating surfactant. Cubic Au-Pd supercrystals show infrared absorption at 3.2 μm due to extensive plasmon coupling. Mie-type resonances centered at 9.8 μm for the Au-Pd supercrystals disappear once the Pd shells are converted into PdH after hydrogen absorption.

BackgroundWe sought to examine whether type 2 diabetes increases the risk of acute organ dysfunction and of hospital mortality following severe sepsis that requires admission to an intensive care unit (ICU).MethodsNationwide population-based retrospective cohort study of 16,497 subjects with severe sepsis who had been admitted for the first time to an ICU during the period of 1998–2008. A diabetic cohort (n = 4573) and a non-diabetic cohort (n = 11924) were then created. Relative risk (RR) of organ dysfunctions, length of hospital stay (LOS), 90-days hospital mortality, ICU resource utilization and hazard ratio (HR) of mortality adjusted for age, gender, Charlson-Deyo comorbidity index score, surgical condition and number of acute organ dysfunction, were compared across patients with severe sepsis with or without diabetes.ResultsDiabetic patients with sepsis had a higher risk of developing acute kidney injury (RR, 1.54; 95% confidence interval (CI), 1.44–1.63) and were more likely to be undergoing hemodialysis (15.55% vs. 7.24%) in the ICU. However, the diabetic cohort had a lower risk of developing acute respiratory dysfunction (RR = 0.96, 0.94–0.97), hematological dysfunction (RR = 0.70, 0.56–0.89), and hepatic dysfunction (RR = 0.77, 0.63–0.93). In terms of adjusted HR for 90-days hospital mortality, the diabetic patients with severe sepsis did not fare significantly worse when afflicted with cardiovascular, respiratory, hepatic, renal and/or neurologic organ dysfunction and by numbers of organ dysfunction. There was no statistically significant difference in LOS between the two cohorts (median 17 vs. 16 days, interquartile range (IQR) 8–30 days, p = 0.11). Multiple logistic regression analysis to predict the occurrence of mortality shows that being diabetic was not a predictive factor with an odds ratio of 0.972, 95% CI 0.890–1.061, p = 0.5203.InterpretationThis large nationwide population-based cohort study suggests that diabetic patients do not fare worse than non-diabetic patients when suffering from severe sepsis that requires ICU admission.