FLYSNPdb: a high-density SNP database of Drosophila melanogaster

FLYSNPdb provides high-resolution single nucleotide polymorphism (SNP) data of Drosophila melanogaster. The database currently contains 27 367 polymorphisms, including >3700 indels (insertions/deletions), covering all major chromsomes. These SNPs are clustered into 2238 markers, which are evenly distributed with an average density of one marker every 50.3 kb or 6.6 genes. SNPs were identified automatically, filtered for high quality and partly manually curated. The database provides detailed information on the SNP data including molecular and cytological locations (genome Releases 3–5), alleles of up to five commonly used laboratory stocks, flanking sequences, SNP marker amplification primers, quality scores and genotyping assays. Data specific for a certain region, particular stocks or a certain genome assembly version are easily retrievable through the interface of a publicly accessible website (http://flysnp.imp.ac.at/flysnpdb.php)

Factors that affect quality of life among people living with HIV attending an urban clinic in Uganda: A cohort study

© 2015 Mutabazi-Mwesigire et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Introduction: With the availability of antiretroviral therapy (ART) and primary general care for people living with HIV (PLHIV) in resource limited settings, PLHIV are living longer, and HIV has been transformed into a chronic illness. People are diagnosed and started on treatment when they are relatively well. Although ART results in clinical improvement, the ultimate goal of treatment is full physical functioning and general well-being, with a focus on quality of life rather than clinical outcomes. However, there has been little research on the relationship of specific factors to quality of life in PLHIV. The objective of this study was to investigate factors associated with quality of life among PLHIV in Uganda receiving basic care and those on ART. Methods: We enrolled 1274 patients attending an HIV outpatient clinic into a prospective cohort study. Of these, 640 received ART. All were followed up at 3 and 6 months. Health related quality of life was assessed with the MOS-HIV Health Survey and the Global Person Generated Index (GPGI). Multivariate linear regression and logistic regression with generalized estimating equations were used to examine the relationship of social behavioral and disease factors with Physical Health Summary (PHS) score, Mental Health Summary (MHS) score, and GPGI. Results: Among PLHIV receiving basic care, PHS was associated with: sex (p=0.045) - females had lower PHS; age in years at enrollment (p=0.0001) - older patients had lower PHS; and depression (

Relationship between CD4 count and quality of life over time among HIV patients in Uganda: A cohort study

© 2015 Mwesigire et al. Background: Immunological markers (CD4 count) are used in developing countries to decide on initiation of antiretroviral therapy and monitor HIV/AIDS disease progression. HIV is an incurable chronic illness, making quality of life paramount. The direct relationship between quality of life and CD4 count is unclear. The purpose of this study is to determine the relationship between change in CD4 count and quality of life measures in a Ugandan cohort of people living with HIV. Methods: We prospectively assessed quality of life among 1274 HIV patients attending an HIV clinic within a national referral hospital over a period of 6months. Quality of life was measured using an objective measure, the Medical Outcomes Study HIV health survey summarized as Physical Health Score and Mental Health Score and a subjective measure, the Global Person Generated Index. Generalized estimating equations were used to analyze the data. The primary predictor variable was change in CD4 count, and the outcome was quality of life scores. We controlled for sociodemographic characteristics, clinical factors and behavioral factors. Twenty in-depth interviews were conducted to assess patient perception of quality of life and factors influencing quality of life. Results: Of the 1274 patients enrolled 1159 had CD4 count at baseline and six months and 586 (51%) received antiretroviral therapy. There was no association found between change in CD4 count and quality of life scores at univariate and multivariate analysis among the study participants whether on or not on antiretroviral therapy. Participants perceived quality of life as happiness and well-being, influenced by economic status, psychosocial factors, and health status. Conclusions: Clinicians and policy makers cannot rely on change in immunological markers to predict quality of life in this era of initiating antiretroviral therapy among relatively healthy patients. In addition to monitoring immunological markers, socioeconomic and psychosocial factors should be underscored in management of HIV patients