Changes in health-related quality of life in older patients with acute myocardial infarction or congestive heart failure: a prospective study

OBJECTIVES: To study changes in health-related quality of life (HR-QL) following acute myocardial infarction (AMI) or congestive heart failure (CHF) in older people (greater than or equal to 57 yr).DESIGN: Prospective cohort Study.SETTING: Primary healthcare registers.PARTICIPANTS: Patients were enrolled on the basis of primary healthcare records. Eighty-nine AMI patients (mean age = 69.5) and 119 CHF patients (mean age = 74.5) were included for analysis.MEASUREMENTS: HR-QL was conceptualized and measured by means of physical (activities of daily living (ADL), instrumental activities of daily living (IADL)), psychological (depressive symptoms, anxiety), social, and role functioning. Premorbid data (TO) were available from a 1993 community-based survey. Incident AMI and CHF cases, developed after 1993, were prospectively followed for 12 months. Assessments were performed at 6 weeks (T1) and 6 (T2) and 12 months (T3) after diagnosis.RESULTS: At the premorbid assessment, AMI patients did not significantly differ on HR-QL from a reference group of older people, whereas CHF patients were on average older and had worse HR-QL compared to the reference group. Although CHF had not yet been diagnosed at TO, symptoms were already present and resulted in decreased levels of functioning. At T1, all HR-QL measures showed worse functioning compared with TO, except for depressive symptoms that presented later (at T2). In contrast to the delay in depressive symptoms, a significant increase in anxiety was already seen at T1. The effect of the somatic conditions was the largest on physical functioning. Effects on psychological and social functioning were less pronounced but still significant. Effects were maintained during the 12 months of follow-up.CONCLUSION: The negative consequences on HR-QL in both AMI and CHF patients are not temporary. No recovery of function was seen in AMI patients, and functioning and CHF patients continued to decline in the first year after diagnosis

A method to impregnate wet soil samples, producing high-quality thin sections.

[120.031]Mineral soils with different moisture content and texture were impregnated successfully by the method described. The water present in the soil sample is replaced by acetone and the acetone-saturated sample is impregnated by FitzPatrick's method (1970) (using acetone as thinner of the polyester resin Synolith 544). Shrinkage is minimal and the thin sections are of high quality. It should be possible to correlate thin section data obtained by this method with physical data obtained from undisturbed material. Micromorphologically, a better understanding of the plasmic fabrics should be possible. (Abstract retrieved from CAB Abstracts by CABI’s permission

Unexpected Magnetism of Small Silver Clusters

The ground-state electronic, structural, and magnetic properties of small silver clusters, Ag$_n$ (2$\le$n$\le$22), have been studied using a linear combination of atomic Gaussian-type orbitals within the density functional theory. The results show that the silver atoms, which are diamagnetic in bulk environment, can be magnetic when they are grouped together in clusters. The Ag$_{13}$ cluster with icosahedral symmetry has the highest magnetic moment per atom among the studied silver clusters. The cluster symmetry and the reduced coordination number specific of small clusters reveal as a fundamental factor for the onset of the magnetism.Comment: 4 pages, 4 figure

Fossil and recent soil formation in lateleistocene loess deposits in the southern part of the Netherlands.

In the younger, Weichselian (Wurm, Wisconsin) loess deposits in the south of the Netherlands there is a horizon with fossil pedotubules (filled animal burrows). These pedotubules, 3 mm ( plus or minus 1 mm) in diameter, unbranched, without preferential orientation, are most abundant in the zone extending from about 30 cm above the decalcification boundary (situated 2-3 m below the soil surface) to some decimeters below it. They are rare in the B3t horizon of the overlying Hapludalf. Their lower extension boundary occurs some 2 m below the decalcification boundary. These tubules result from fossil animal activity followed by decalcification, mainly of a Boelling soil surface. A loess layer some 2 m thick was probably deposited in the post-Boelling period; the lower part of this layer has a lamellae spot zone. Micromorphological evidence shows that the lamellae spot zone was originally at the actual soil surface. Consequently a geogenic origin of the lamellae spot zone rather than a pedogenic origin seems likely. Soil formation subsequently intensified the textural differences. It is suggested that the post-Boelling loess was free of calcium carbonate at the beginning of the Holocene and was deposited non-calcareously or was decalcified synsedimentarily. In Western Europe, loess soils formed from Pleistocene deposits probably matured earlier in the Holocene than has been assumed hitherto. (Abstract retrieved from CAB Abstracts by CABI’s permission

Deep analysis of EIT dataset to classify apnea and non-apnea cases in neonatal patients

Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods; a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates' EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features