Determinants for a low health-related quality of life in asthmatics

People with asthma suffer from impaired health-related quality of life (HRQL), but the determinants of HRQL among asthmatics are not completely understood. The aim of this investigation was to study determinants of low HRQL in asthmatics and to study whether the determinants of HRQL differ between sexes and age groups. A cohort of three age groups in Sweden was investigated in 1990 using a questionnaire with focus on respiratory symptoms. To study quality of life, the generic instrument Gothenburg Quality of Life was used. The participants were also investigated with interviews, spirometry, and allergy testing. Asthma was diagnosed in 616 subjects. Fifty-eight per cent (n = 359) of the subjects were women; and 24% were smokers, 22% ex-smokers, and 54% were non-smokers. Women were more likely than men to report poor health-related quality of life. Respiratory symptoms severity was another independent determinant of a lower quality of life as well as airway responsiveness to irritants. Current and former smokers also reported lower quality of life. Finally, absenteeism from school and work was associated with lower quality of life. Factors such as sex, smoking habits, airway responsiveness to irritants, respiratory symptom severity, allergy, and absenteeism from school and work were associated with low HRQL in asthmatics

Genetic and expression studies of SMN2 gene in Russian patients with spinal muscular atrophy type II and III

<p>Abstract</p> <p>Background</p> <p>Spinal muscular atrophy (SMA type I, II and III) is an autosomal recessive neuromuscular disorder caused by mutations in the survival motor neuron gene (<it>SMN1</it>). <it>SMN2 </it>is a centromeric copy gene that has been characterized as a major modifier of SMA severity. SMA type I patients have one or two <it>SMN2 </it>copies while most SMA type II patients carry three <it>SMN2 </it>copies and SMA III patients have three or four <it>SMN2 </it>copies. The <it>SMN1 </it>gene produces a full-length transcript (FL-SMN) while <it>SMN2 </it>is only able to produce a small portion of the FL-SMN because of a splice mutation which results in the production of abnormal SMNΔ7 mRNA.</p> <p>Methods</p> <p>In this study we performed quantification of the <it>SMN2 </it>gene copy number in Russian patients affected by SMA type II and III (42 and 19 patients, respectively) by means of real-time PCR. Moreover, we present two families consisting of asymptomatic carriers of a homozygous absence of the <it>SMN1 </it>gene. We also developed a novel RT-qPCR-based assay to determine the FL-SMN/SMNΔ7 mRNA ratio as SMA biomarker.</p> <p>Results</p> <p>Comparison of the <it>SMN2 </it>copy number and clinical features revealed a significant correlation between mild clinical phenotype (SMA type III) and presence of four copies of the <it>SMN2 </it>gene. In both asymptomatic cases we found an increased number of <it>SMN2 </it>copies in the healthy carriers and a biallelic <it>SMN1 </it>absence. Furthermore, the novel assay revealed a difference between SMA patients and healthy controls.</p> <p>Conclusions</p> <p>We suggest that the <it>SMN2 </it>gene copy quantification in SMA patients could be used as a prognostic tool for discrimination between the SMA type II and SMA type III diagnoses, whereas the FL-SMN/SMNΔ7 mRNA ratio could be a useful biomarker for detecting changes during SMA pharmacotherapy.</p

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design