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.

Although various cell encapsulation materials are available commercially for a wide range of potential therapeutic cells, their combined clinical impact remains inconsistent. Synthetic materials such as poly(ethylene glycol) (PEG) hydrogels are mechanically robust and have been extensively explored but lack natural biofunctionality. Naturally derived materials including collagen, fibrin and alginate-chitosan are often labile and mechanically weak. In this paper we report the development of a hybrid biomatrix based on the thiol-ene reaction of PEG diacrylate (PEGdA) and cysteine/PEG-modified gelatin (gel-PEG-Cys). We hypothesized that covalent crosslinking decreases gelatin dissolution thus increasing gelatin resident time within the matrix and the duration of its biofunctionality; at the same time the relative ratio of PEGdA to gel-PEG-Cys in the matrix formulation directly affects hydrogel bulk and local microenvironment properties. Bulk viscoelastic properties were highly dependent on PEGdA concentration and total water content, while gel-PEG-Cys concentration was more critical to swelling profiles. Microviscoelastic properties were related to polymer concentration. The covalently crosslinked gel-PEG-Cys with PEGdA decreased gelatin dissolution out of the matrix and collagenase-mediated degradation. Fibroblasts and keratinocyte increased adhesion density and formed intercellular connections on stiffer hydrogel surfaces, while cells exhibited more cytoplasmic spreading and proliferation when entrapped within softer hydrogels. Hence, this material system contains multiparametric factors that can easily be controlled to modulate the chemical, physical and biological properties of the biomatrix for soft tissue scaffolding and cell presentation to reconstruct lost tissue architecture and physical functionality.

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.

Objective The epidemiology of psychiatric symptoms among COVID-19 patients is poorly characterized. This paper seeks to identify the prevalence of anxiety, depression, and acute stress disorder among hospitalized patients with COVID-19. Methods Adult patients recently admitted to non-ICU medical ward settings with COVID-19 were eligible for enrollment. Enrolled patients were screened for depression, anxiety, and delirium. Subsequently, patients were followed by phone after two weeks and re-screened for depression, anxiety, and acute stress disorder symptoms. Subjects’ medical records were abstracted for clinical data. Results 58 subjects were enrolled of whom 44 completed the study. Initially, 36% of subjects had elevated anxiety symptoms and 29% elevated depression symptoms. At two-week follow-up, 9% had elevated anxiety symptoms, 20% elevated depression symptoms, and 25% mild-to-moderate acute stress disorder symptoms. Discharge to home was not associated with improvement in psychiatric symptoms. Conclusion A significant number of patients hospitalized with COVID-19 experience symptoms of depression and anxiety. While anxiety improves following index admission, depression remains fairly stable. Furthermore, a significant minority of patients experience acute stress disorder symptoms, though these are largely mild-to-moderate.

BackgroundOver the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency.ResultsM2DB is a human curated microarray database designed for easy querying, based on clinical information and for interactive retrieval of either raw or uniformly pre-processed data, along with a set of quality-control metrics. The database contains more than 10,000 previously published Affymetrix GeneChip arrays, performed using human clinical specimens. M2DB allows online querying according to a flexible combination of five clinical annotations describing disease state and sampling location. These annotations were manually curated by controlled vocabularies, based on information obtained from GEO, ArrayExpress, and published papers. For array-based assessment control, the online query provides sets of QC metrics, generated using three available QC algorithms. Arrays with poor data quality can easily be excluded from the query interface. The query provides values from two algorithms for gene-based filtering, and raw data and three kinds of pre-processed data for downloading.ConclusionM2DB utilizes a user-friendly interface for QC parameters, sample clinical annotations, and data formats to help users obtain clinical metadata. This database provides a lower entry threshold and an integrated process of meta-analysis. We hope that this research will promote further evolution of microarray meta-analysis.