A matrix for the qualitative evaluation of nursing tasks

Aims To formulate a model for patient–nurse interaction; to compile a comprehensive list of nursing tasks on hospital wards; and to construct a nursing tasks demand matrix. Background The physical demands associated with nursing profession are of growing interest among researchers. Yet, it is the complexity of nursing tasks that defines the demands of ward nurses’ role. This study explores nursing tasks, based on patient–nurse interaction on hospital wards. Methods Extant literature was reviewed to formulate a patient–nurse interaction model. Twenty ward nurses were interviewed to compile a list of nursing tasks. These nursing tasks were mapped against the patient–nurse interaction model. Results A patient–nurse interaction model was created, consisting of: (1) patient care, (2) patient surveillance and (3) patient support. Twenty-three nursing tasks were identified. The nursing tasks demand matrix was constructed. Conclusions Ward managers may use a nursing tasks demand matrix to determine the demands of nursing tasks on ward nurses. Implications for Nursing Management While many studies have explored either the physical or the psychosocial aspects of nursing tasks separately, this study suggests that the physicality of nursing tasks must be evaluated in tandem with their complexity. Ward managers may take a holistic approach to nursing tasks evaluation by using a nursing tasks demand matrix

Prediction of Alzheimer's Disease Using a Cerebrospinal Fluid Pattern of C-Terminally Truncated β-Amyloid Peptides

Background: Identifying individuals at high risk of developing Alzheimer’s disease (AD) is important for future therapeutic strategies, and there is a clinical need for diagnostic biomarkers to identify incipient AD. Objective: The aim of the present study was to investigate if the AD-associated A _ peptide pattern recently found in cerebrospinal fluid (CSF) could discriminate between patients with incipient AD and those with stable mild cognitive impairment (MCI) by analyzing CSF from patients with MCI at baseline. Methods: The levels of A _ 1-37, -38, -39, -40, -42 were analyzed by A _ -SDS-PAGE/ immunoblot in CSF from 19 healthy controls, 25 patients with stable MCI and from 25 patients with MCI who later developed AD during 4- to 6-year follow-up. Results: All healthy controls and 20 out of 22 patients who developed AD were correctly classified by their baseline A _ peptide pattern. In 9 out of 25 stable MCI patients, the pattern indicated incipient AD in spite of clinical nonconversion. Interestingly, these individuals had apolipoprotein E genotypes and CSF levels of tau and phospho-tau that are known to be associated with high risk of AD. Conclusion: Altogether, our study reveals the novel finding that the A _ peptide pattern is able to predict AD in patients with MCI with a sensitivity of 91% and specificity of 64%. The specificity would increase to 94% if the high-risk patients in the stable MCI cohort developed AD during extended follow-up

A Key Commitment Step in Erythropoiesis Is Synchronized with the Cell Cycle Clock through Mutual Inhibition between PU.1 and S-Phase Progression

During red blood cell development, differentiation and cell cycle progression are intimately and uniquely linked through interdependent mechanisms involving the erythroid transcriptional suppressor PU.1 and the cyclin-dependent kinase inhibitor p57KIP2

Integrated Device-Fabric Explorations and Noise Impact and Mitigation in Nanoscale Fabrics

An integrated device-fabric methodology for evaluating and validating nanoscale computing fabrics is presented. The methodology integrates physical layer assumptions for materials and device structures with accurate 3-D simulations of device electrostatics and operations and circuit-level noise and cascading validations. Electrical characteristics of six different crossed nanowire field-effect transistors (xnwFETs) are simulated and current and capacitance data are obtained. Behavioral models incorporating device data are generated and used in fabric level simulations to evaluate noise implications of devices and sequencing schemes. Device characteristics are found to have different implications for logic “1” and logic “0” noise with faster devices being more (less) resilient to logic “1” (logic “0”) noise. A new noise resilient dynamic sequencing scheme is presented which isolates logic “0” noise events and prevents them from propagating to cascaded circuit stages, thereby enabling faster devices. Performance implications and optimizations for fabrics incorporating the new noise resilient scheme are discussed. The scheme is also analyzed and validated against an external noise source (power supply drooping). These results show that noise resilient nanofabrics can be designed through a combination of device engineering and fabric-level optimizations of the sequencing scheme. Performance optimizations and implications of device and physical layer assumptions on manufacturing are discussed

Dividend Privileges, Measurement Errors, and the Value of Voting Rights: Evidence from Italy

The present paper proposes a new measure of the voting right, the Relative Vote Segment, which incorporates dividend privileges into the inferior class of shares. We test and compare it against the standard Relative Price Difference and the Nenova (2003) measure using 1998\u2013 2008 data from Italy, a country where dividend privileges are relevant. Results show that when dividend privileges are considered, the average voting right equals +35.63%, while its estimated value corresponds to a significantly lower +20.35% and +1.29% with the Relative Price Difference and the Nenova (2003) measure, respectively. Negative values of voting rights drop significantly with our methodology. Results become even more clear-cut when we clean the sample of possible measurement errors. As far as the determinants of the voting premium are concerned, the choice of the measure does not appear to have a significant impact, as long as the dividend differences are controlled for