Hongsargent8143

Transfection with miR-34a mimic also reduced the mRNA and protein expression of ACSL4 and lipogenesis genes, including PPARγ, aP2, and SREBP-1C, but increased the expression of steatolysis genes such as ATGL and Sirt1. In contrast, the miR-34a inhibitor had the opposite effect on gene expression. Further, knockdown of ACSL4 decreased lipid droplet accumulation. CONCLUSIONS Our results support the hypothesis that miR-34a regulates intramuscular fat deposition in porcine adipocytes by targeting ACSL4.BACKGROUND Sero- prevalence studies often have a problem of missing data. Few studies report the proportion of missing data and even fewer describe the methods used to adjust the results for missing data. The objective of this review was to determine the analytical methods used for analysis in HIV surveys with missing data. METHODS We searched for population, demographic and cross-sectional surveys of HIV published from January 2000 to April 2018 in Pub Med/Medline, Web of Science core collection, Latin American and Caribbean Sciences Literature, Africa-Wide Information and Scopus, and by reviewing references of included articles. All potential abstracts were imported into Covidence and abstracts screened by two independent reviewers using pre-specified criteria. Disagreements were resolved through discussion. A piloted data extraction tool was used to extract data and assess the risk of bias of the eligible studies. Data were analysed through a quantitative approach; variables were presented and summarised ution. Our review outlined a number of methods that can be used to adjust for missing data on HIV studies; however, more information and awareness are needed to allow informed choices on which method to be applied for the estimates to be more reliable and representative.BACKGROUND Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. RESULTS We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least ony revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.BACKGROUND The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. RESULTS We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. selleck chemicals The machine learning techniques help to identify the motions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.BACKGROUND The family Aegisthidae is known as typical component of deep-sea hyperbenthic waters that gradually colonized other marine environments. The phylogenetic relationships within this family have been examined here including hyperbenthic, planktonic, benthic forms and two associated Aegisthidae species. RESULTS Ninety four specimens belong to 14 genera were studied using 18S and 28S rRNA and COI mtDNA. Bayesian analysis supports the monophyly of 10 genera whereas Andromastax, Jamstecia, Nudivorax and Aegisthus revealed to be paraphyletic. The first offshoot of the phylogenetic tree is a clade consists of the undescribed genus Aegisthidae gen.1 sister to the two monophyletic genera Cerviniella and Hase, whereas the other Cerviniinae members (represented by Cervinia and Expansicervinia) assemble a monophylum, sister to the hyperbenthic and planktonic aegisthid genera, resulting in the paraphyly of the subfamily Cerviniinae. Hence, we defined the new subfamily Cerviniellinae subfam. nov. encompassing the . The planktonic Aegisthus, Andromastax, Jamstecia, Nudivorax and Scabrantenna confirm the monophylom Aegisthinae, sister to the Pontostratiotinae. CONCLUSIONS Our DNA based phylogeny reveals the deep-sea origin of Aegisthidae by placing benthic Aegisthidae gen.1 and Cerviniellinae subfam. nov. as the most basal lineages. Secondary adaptations to hyperbenthic and planktonic realms, as well as associated lifestyle were discovered here by the derived positions of Pontostratiotinae, Aegisthinae and Siphonis respectively.BACKGROUND Non-participation and attrition are rarely studied despite being important methodological issues when performing post-disaster studies. A longitudinal survey of civilians exposed to the January 2015 terrorist attacks in Paris, France, was conducted 6 (Wave 1) and 18 months (Wave 2) after the attacks. We described non-participation in Wave 1 and determined the factors associated with attrition in Wave 2. METHODS Multivariate logistic regression models were used to compare participants in both waves with those who participated in the first wave only. Analyses were performed taking the following factors into account socio-demographic characteristics, exposure to terror, peri-traumatic reactions, psychological support, perceived social support, impact on work, social and family life, and mental health disorders. Characteristics of new participants in Wave 2 were compared with participants in both waves using a chi-square test. RESULTS Of the 390 persons who were eligible to participate in the survey, 190 participated in Wave 1 (participation rate 49%).