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Abstract Background Behavioural and structural factors related to sex work, place female sex workers (FSWs) at high risk of maternal mortality and morbidity (MMM), with a large portion due to unintended pregnancies and abortions. In the African context where MMM is the highest in the world, understanding the frequency and determinants of pregnancy and abortion among FSWs is important in order to meet their sexual and reproductive health needs. Methods Data from two Beninese cross-sectional surveys among FSWs aged 18+ (2013, N = 450; 2016, N = 504) were merged. We first performed exploratory univariate analyses to identify factors associated with pregnancy and abortion (p

Abstract HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.

Abstract Background High quality genetic material is an essential pre-requisite when analyzing gene expression using microarray technology. Peripheral blood mononuclear cells (PBMC) are frequently used for genomic analyses, but several factors can affect the integrity of nucleic acids prior to their extraction, including the methods of PBMC collection and isolation. Due to the lack of the relevant data published, we compared the Ficoll-Paque density gradient centrifugation and BD Vacutainer cell preparation tube (CPT) protocols to determine if either method offered a distinct advantage in preparation of PBMC-derived immune cell subsets for their use in gene expression analysis. We evaluated the yield and purity of immune cell subpopulations isolated from PBMC derived by both methods, the quantity and quality of extracted nucleic acids, and compared gene expression in PBMC and individual immune cell types from Ficoll and CPT isolation protocols using Affymetrix microarrays. Results The mean yield and viability of fresh PBMC acquired by the CPT method (1.16 × 106 cells/ml, 93.3 %) were compatible to those obtained with Ficoll (1.34 × 106 cells/ml, 97.2 %). No differences in the mean purity, recovery, and viability of CD19+ (B cells), CD8+ (cytotoxic T cells), CD4+ (helper T cell) and CD14+ (monocytes) positively selected from CPT- or Ficoll-isolated PBMC were found. Similar quantities of high quality RNA and DNA were extracted from PBMC and immune cells obtained by both methods. Finally, the PBMC isolation methods tested did not impact subsequent recovery and purity of individual immune cell subsets and, importantly, their gene expression profiles. Conclusions Our findings demonstrate that the CPT and Ficoll PBMC isolation protocols do not differ in their ability to purify high quality immune cell subpopulations. Since there was no difference in the gene expression profiles between immune cells obtained by these two methods, the Ficoll isolation can be substituted by the CPT protocol without conceding phenotypic changes of immune cells and compromising the gene expression studies. Given that the CPT protocol is less elaborate, minimizes cells’ handling and processing time, this method offers a significant operating advantage, especially in large-scale clinical studies aiming at dissecting gene expression in PBMC and PBMC-derived immune cell subpopulations.

Additional Figure 4. MRI (top), FDG PET (middle), tau PET (bottom) comparisons between young CN and EOnonAD and old CN and LOnonAD groups. The significance maps show p

Droplet digital PCR H3K27M detection validation raw droplet counts. (XLSX 12Â kb)

Abstract Background PAX6 is a homeodomain transcription factor that acts in a highly dosage-sensitive manner to regulate the development and function of the eyes, nose, central nervous system, gut, and endocrine pancreas. Several individual microRNAs (miRNA) have been implicated in regulating PAX6 in different cellular contexts, but a more general view of how they contribute to the fine-tuning and homeostasis of PAX6 is poorly understood. Results Here, a comprehensive analysis of the Pax6 3′ untranslated region was performed to map potential miRNA recognition elements and served as a backdrop for miRNA expression profiling experiments to identify potential cell/tissue-specific miRNA codes. Pax6 3’UTR pull-down studies identified a cohort of miRNA interactors in pancreatic αTC1–6 cells that, based on the spacing of their recognition sites in the Pax6 3’UTR, revealed 3 clusters where cooperative miRNA regulation may occur. Some of these interacting miRNAs have been implicated in α cell function but have not previously been linked to Pax6 function and may therefore represent novel PAX6 regulators. Conclusions These findings reveal a regulatory landscape upon which miRNAs may participate in the developmental control, fine-tuning and/or homeostasis of PAX6 levels.

Additional file 2: Table S2. Prevalence of classified genera in datasets processed by Qiime1 and Qiime2 after excluding OTUs/ASVs with less than 20 reads across each dataset. Table S4. Prevalence of classified genera in datasets processed by Qiime1 and Qiime2 after excluding OTUs/ASVs with less than 20 reads across each dataset and mean relative abundance of less than 0.01%.