103 research outputs found

    Predictive validity of a brief antiretroviral adherence index: Retrospective cohort analysis under conditions of repetitive administration

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    <p>Abstract</p> <p>Background</p> <p>Newer antiretroviral (ARV) agents have improved pharmacokinetics, potency, and tolerability and have enabled the design of regimens with improved virologic outcomes. Successful antiretroviral therapy is dependent on patient adherence. In previous research, we validated a subset of items from the ACTG adherence battery as prognostic of virologic suppression at 6 months and correlated with adherence estimates from the Medication Event Monitoring System (MEMS). The objective of the current study was to validate the longitudinal use of the Owen Clinic adherence index in analyses of time to initial virologic suppression and maintenance of suppression.</p> <p>Results</p> <p>278 patients (naïve n = 168, experienced n = 110) met inclusion criteria. Median [range] time on the first regimen during the study period was 286 (30 – 1221) days. 217 patients (78%) achieved an undetectable plasma viral load (pVL) at median 63 days. 8.3% (18/217) of patients experienced viral rebound (pVL > 400) after initial suppression. Adherence scores varied from 0 – 25 (mean 1.06, median 0). The lowest detectable adherence score cut point using this instrument was ≥ 5 for both initial suppression and maintenance of suppression. In the final Cox model of time to first undetectable pVL, controlling for prior treatment experience and baseline viral load, the adjusted hazard ratio for time updated adherence score was 0.36<sub>score ≥ 5 </sub>(95% CI: 0.19–0.69) [reference: <5]. In the final generalized estimating equations (GEE) logistic regression model the adjusted odds ratio for time-updated adherence score was 0.17<sub>score ≥ 5 </sub>(0.05–0.66) [reference: <5].</p> <p>Conclusion</p> <p>A brief, longitudinally administered self report adherence instrument predicted both initial virologic suppression and maintenance of suppression in patients using contemporary ARV regimens. The survey can be used for identification of sub-optimal adherence with subsequent appropriate intervention.</p

    IoT-based BIM integrated model for energy and water management in smart homes

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    Increasing urbanization and growth in infrastructure create a demand to utilize modern tools to manage human needs. Effective integration of the Internet of Things (IoT) into the design of smart homes is an actively growing area in the construction industry. The ever-increasing demand and cost of energy require a smart solution in the design stage by the construction industry. It is possible to reduce household energy consumption by utilizing energy-efficient sustainable materials in infrastructure construction. Building Information Modeling (BIM) can provide a solution to effectively manage energy. The integration of the IoT further improves the design of comfortable smart homes by utilizing natural lighting. BIM aids in determining energy efficiency and making decisions by presenting the user with several design options via the 6D method. The present study considered a sample home design following the National Building Code (NBC) and American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) standards for implementation. Natural lighting analysis is carried out with the tool Insight 360 to analyze the energy consumption of the building. Some of the outputs obtained from the analysis are wall-to-window ratio (WWR), window shades, design options for window glass, energy use intensity (EUI), and annual energy cost (AEC). The results of the outputs are compared to find the energy-efficient optimum natural lighting of the proposed building. The lesser EUI (16%–21%) and AEC (23%–28%) are identified with the utilization of low emissivity glass in window panels compared with other types of glass. The proposed IoT-based BIM integration model proves that the effective utilization of natural lighting reduces overall household energy consumption

    Expanding the genotypic and phenotypic spectrum of severe serine biosynthesis disorders.

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    Serine biosynthesis disorders comprise a spectrum of very rare autosomal recessive inborn errors of metabolism with wide phenotypic variability. Neu-Laxova syndrome represents the most severe expression and is characterized by multiple congenital anomalies and pre- or perinatal lethality. Here, we present the mutation spectrum and a detailed phenotypic analysis in 15 unrelated families with severe types of serine biosynthesis disorders. We identified likely disease-causing variants in the PHGDH and PSAT1 genes, several of which have not been reported previously. Phenotype analysis and a comprehensive review of the literature corroborates the evidence that serine biosynthesis disorders represent a continuum with varying degrees of phenotypic expression and suggest that even gradual differences at the severe end of the spectrum may be correlated with particular genotypes. We postulate that the individual residual enzyme activity of mutant proteins is the major determinant of the phenotypic variability, but further functional studies are needed to explore effects at the enzyme protein level.We are indebted to all families for participating in this study. We would like to acknowledge Dr. Natasha Laidlew, who initially suggested the diagnosis in one of the cases and provided important phenotypic information, and Dr. María-Luisa Martínez-Fernández for the critical management of biosamples in ECEMC Program of Spain. Financial assistance was received in support of the study by grants from the German Federal Ministry of Education and Research (BMBF) (GeNeRARe, FKZ: 01GM1519D) to M. Z. and from the Institute of Health Carlos III: Convenio ISCIII-ASEREMAC, and Fundación 1000 sobre Defectos Congénitos, of Spain to E. B.-S. and I. R. G.S
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