58 research outputs found

    Adherence to antiretroviral therapy and the associated factors among people living with HIV/AIDS in Northern Peru : a cross-sectional study

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    Altres ajuts: This study was partially funded by a research Grant from the Fundació Autònoma Solidària (Resolució Convocatòria FSXXXIII-Peru); and the Oficina de Investigación y Creatividad Intelectual from the Universidad María Auxiliadora (OICI-001-2017).There are approximately 72,000 people living with HIV/AIDS (PLHIV) in Peru. Non-adherence to antiretroviral therapy (ART) is the most important factor for therapeutic failure and the development of resistance. Peru has achieved moderate progress in meeting the 90-90-90 targets, but only 60% of PLHIV receiving ART are virally suppressed. The purpose of this study was to understand ART adherence in the Peruvian context, including developing sociodemographic and clinical profiles, evaluating the clinical management strategies, and analyzing the relationships between the variables and adherence of PLHIV managed at a regional HIV clinic in Lambayeque Province (Northern Peru). This was a cross-sectional study with 180 PLHIV adults, non-randomly but consecutively selected with self-reported ART compliance (78.2% of the eligible population). The PLHIV profile (PLHIV-Pro) and the Simplified Medication Adherence Questionnaire (SMAQ) were used to collect sociodemographic information, clinical variables, and data specific to ART adherence. Descriptive analysis of sociodemographic and clinical characteristics was performed. Bivariate analysis was performed with the Mann-Whitney test, Chi square test, and Yates correction. The 180 PLHIV sample included 78.9% men, 49.4% heterosexual, 45% with a detectable HIV-1 viral load less than 40 copies/ml, 58.3% not consistently adherent, and only 26.1% receiving Tenofovir + Lamivudine + Efavirenz. Risk factors significant for non-adherence included concurrent tuberculosis, discomfort with the ART regime, and previous pauses in ART. Multivariate analysis of nested models indicated having children is a protector factor for adherence. Self-reported adherence appeared to be low and the use of first-line therapy is not being prescribed homogeneously. Factors associated with nonadherence are both medical and behavioral, such as having tuberculosis, pausing ART, or experiencing discomfort with ART. The Peruvian government needs to update national technical standards, monitor medication availability, and provide education to health care professionals in alignment with evidence-based guidelines and international recommendations. Instruments to measure adherence need to be developed and evaluated for use in Latin America

    Impact of hospital characteristics on implementation of a Pediatric Early Warning System in resource-limited cancer hospitals

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    BackgroundPediatric Early Warning Systems (PEWS) aid in identification of deterioration in hospitalized children with cancer but are underutilized in resource-limited settings. Proyecto EVAT is a multicenter quality improvement (QI) collaborative in Latin America to implement PEWS. This study investigates the relationship between hospital characteristics and time required for PEWS implementation.MethodsThis convergent mixed-methods study included 23 Proyecto EVAT childhood cancer centers; 5 hospitals representing quick and slow implementers were selected for qualitative analysis. Semi-structured interviews were conducted with 71 stakeholders involved in PEWS implementation. Interviews were recorded, transcribed and translated to English, then coded using a priori and novel codes. Thematic content analysis explored the impact of hospital characteristics and QI experience on time required for PEWS implementation and was supplemented by quantitative analysis exploring the relationship between hospital characteristics and implementation time.ResultsIn both quantitative and qualitative analysis, material and human resources to support PEWS significantly impacted time to implementation. Lack of resources produced various obstacles that extended time necessary for centers to achieve successful implementation. Hospital characteristics, such as funding structure and type, influenced PEWS implementation time by determining their resource-availability. Prior hospital or implementation leader experience with QI, however, helped facilitate implementation by assisting implementers predict and overcome resource-related challenges.ConclusionsHospital characteristics impact time required to implement PEWS in resource-limited childhood cancer centers; however, prior QI experience helps anticipate and adapt to resource challenges and more quickly implement PEWS. QI training should be a component of strategies to scale-up use of evidence-based interventions like PEWS in resource-limited settings

    Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

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    A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk

    Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

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    To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

    Robust estimation of bacterial cell count from optical density

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    Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

    Finishing the euchromatic sequence of the human genome