Crowellvest0226

Background When conducting an Overviews of Reviews on health-related topics, it is unclear which combination of bibliographic databases authors should use for searching for SRs. Our goal was to determine which databases included the most systematic reviews and identify an optimal database combination for searching systematic reviews. Methods A set of 86 Overviews of Reviews with 1219 included systematic reviews was extracted from a previous study. Inclusion of the systematic reviews was assessed in MEDLINE, CINAHL, Embase, Epistemonikos, PsycINFO, and TRIP. The mean inclusion rate (% of included systematic reviews) and corresponding 95% confidence interval were calculated for each database individually, as well as for combinations of MEDLINE with each other database and reference checking. Results Inclusion of systematic reviews was higher in MEDLINE than in any other single database (mean inclusion rate 89.7%; 95% confidence interval [89.0-90.3%]). Combined with reference checking, this value increased to 93.7% [93.2-94.2%]. The best combination of two databases plus reference checking consisted of MEDLINE and Epistemonikos (99.2% [99.0-99.3%]). Stratification by Health Technology Assessment reports (97.7% [96.5-98.9%]) vs. Cochrane Overviews (100.0%) vs. non-Cochrane Overviews (99.3% [99.1-99.4%]) showed that inclusion was only slightly lower for Health Technology Assessment reports. However, MEDLINE, Epistemonikos, and reference checking remained the best combination. Among the 10/1219 systematic reviews not identified by this combination, five were published as websites rather than journals, two were included in CINAHL and Embase, and one was included in the database ERIC. Conclusions MEDLINE and Epistemonikos, complemented by reference checking of included studies, is the best database combination to identify systematic reviews on health-related topics.Background Publication and related biases (including publication bias, time-lag bias, outcome reporting bias and p-hacking) have been well documented in clinical research, but relatively little is known about their presence and extent in health services research (HSR). This paper aims to systematically review evidence concerning publication and related bias in quantitative HSR. Methods Databases including MEDLINE, EMBASE, HMIC, CINAHL, Web of Science, Health Systems Evidence, Cochrane EPOC Review Group and several websites were searched to July 2018. Information was obtained from (1) Methodological studies that set out to investigate publication and related biases in HSR; (2) Systematic reviews of HSR topics which examined such biases as part of the review process. Relevant information was extracted from included studies by one reviewer and checked by another. Studies were appraised according to commonly accepted scientific principles due to lack of suitable checklists. Data were synthesised narratively. Results After screening 6155 citations, four methodological studies investigating publication bias in HSR and 184 systematic reviews of HSR topics (including three comparing published with unpublished evidence) were examined. Evidence suggestive of publication bias was reported in some of the methodological studies, but evidence presented was very weak, limited in both quality and scope. Reliable data on outcome reporting bias and p-hacking were scant. HSR systematic reviews in which published literature was compared with unpublished evidence found significant differences in the estimated intervention effects or association in some but not all cases. Conclusions Methodological research on publication and related biases in HSR is sparse. Evidence from available literature suggests that such biases may exist in HSR but their scale and impact are difficult to estimate for various reasons discussed in this paper. JAK inhibitor Systematic review registration PROSPERO 2016 CRD42016052333.Background The systematic interrogation of reproduction-related genes was key to gain a comprehensive understanding of the molecular mechanisms underlying male reproductive traits in mammals. Here, based on the data collected from the NCBI SRA database, this study first revealed the genes involved in porcine male reproduction as well their uncharacterized transcriptional characteristics. Results Results showed that the transcription of porcine genome was more widespread in testis than in other organs (the same for other mammals) and that testis had more tissue-specific genes (1210) than other organs. GO and GSEA analyses suggested that the identified test is-specific genes (TSGs) were associated with male reproduction. Subsequently, the transcriptional characteristics of porcine TSGs, which were conserved across different mammals, were uncovered. Data showed that 195 porcine TSGs shared similar expression patterns with other mammals (cattle, sheep, human and mouse), and had relatively higher transcription abundances and tissue specificity than low-conserved TSGs. Additionally, further analysis of the results suggested that alternative splicing, transcription factors binding, and the presence of other functionally similar genes were all involved in the regulation of porcine TSGs transcription. Conclusions Overall, this analysis revealed an extensive gene set involved in the regulation of porcine male reproduction and their dynamic transcription patterns. Data reported here provide valuable insights for a further improvement of the economic benefits of pigs as well as future treatments for male infertility.Background Use of genomic tools to characterize wildlife populations has increased in recent years. In the past, genetic characterization has been accomplished with more traditional genetic tools (e.g., microsatellites). The explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of increased precision in population genetic parameter estimates and identification of demographically and evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic tools. At present, few empirical comparisons of population genetic parameters and clustering analyses performed with microsatellites and SNPs have been conducted. Results Here we used microsatellite and SNP data generated from Gunnison sage-grouse (Centrocercus minimus) samples to evaluate concordance of the results obtained from each dataset for common metrics of genetic diversity (HO, HE, FIS, AR) and differentiation (FST, GST, DJost). Additionally, we evaluated clustering of individuals using putatively neutral (SNPs and microsatellites), putatively adaptive, and a combined dataset of putatively neutral and adaptive loci.