Effects of the prolonged administration of bradykinin on the rat pituitary-adrenocortical axis

The effects of a prolonged administration of bradykinin (BK) and/or D-Arg, [Hyp3, D-Phe7]-BK, a specific antagonist of BK receptors (BK-A) (daily subcutaneous injections of 4 nmol/rat for 6 days) on the function of the pituitary-adrenocortical axis were investigated. BK did not change plasma aldosterone concentration (PAC), but markedly lowered that of corticosterone (PBC) and consequently induced a compensatory hypersecretion of ACTH by the pituitary gland. BK-A did not apparently affect the function and growth of the adrenal gland, but, when administered together with BK, markedly raised both PAC and PBC, and provoked a significant atrophy of the adrenal gland, probably due to loss of parenchymal cells. Taken together, these rather puzzling findings do not appear to provide clear evidence for the involvement of BK in the physiological regulation of adrenocortical growth and steroidogenic capacity in rats.     

An infrastructure for smart hospitals

In healthcare facilities most of the daily activities require strict coordination between clinicians, who often operate under heavy workloads and minimal workforce conditions in environments filled with increasingly complex technology. Ubiquitous Computing applications constitute a suitable solution for both reducing medical costs and improving patient safety by better supporting clinical processes. In this study we introduce an intelligent infrastructure for smart hospitals which implements basic services to optimize medical staff/patient interactions and grants ubiquitous and transparent access to clinical data stored in standard clinical databases. This infrastructure relies on the integration of Radio Frequency IDentification (RFID) and photosensor technologies to identify, locate and track clinicians and patients equipped with mobile devices and wearable RFID tags.     

Consistent estimation of the memory parameter for nonlinear time series

Abstract. For linear processes, semiparametric estimation of the memory parameter, based on the log‐periodogram and local Whittle estimators, has been exhaustively examined and their properties well established. However, except for some specific cases, little is known about the estimation of the memory parameter for nonlinear processes. The purpose of this paper is to provide the general conditions under which the local Whittle estimator of the memory parameter of a stationary process is consistent and to examine its rate of convergence. We show that these conditions are satisfied for linear processes and a wide class of nonlinear models, among others, signal plus noise processes, nonlinear transforms of a Gaussian process ξ_t and exponential generalized autoregressive, conditionally heteroscedastic (EGARCH) models. Special cases where the estimator satisfies the central limit theorem are discussed. The finite‐sample performance of the estimator is investigated in a small Monte Carlo study.     

Correlation of reactivity with chemical structure: Thermal hydrogenation of gas oils

The relationship between chemical structure and reactivity for thermal hydroprocessing was studied for five gas oils derived from Alberta bitumens. Chemical structure was characterized by combining data from ¹ H and ¹³ C nuclear magnetic resonance spectroscopy, class fractionation, and elemental analysis to calculate structural parameters. Thermal hydrotreating was performed in a continuous-flow stirred reactor at 420 and 440°C, 13.9 MPa hydrogen pressure, and 1.5 h¹ LHSV. Conversion of the 343–525°C boiling fraction of the gas oils was correlated with the concentration of naphthenic methylene groups in the feed. Formation of methane and ethane was dependent on the degree of condensation of the aromatic rings in the feed oils. Thermal desulfurization was highly correlated with the amount of saturates in the feed, and the aromaticity of the resin fraction. Hydrogen consumption increased with the aromaticity of the gas oil…     

A Bayesian approach to estimate the marginal loss distributions in operational risk management

One of the main problems in operational risk management is the lack of loss data, which affects the parameter estimates of the marginal distributions of the losses. The principal reason is that financial institutions only started to collect operational loss data a few years ago, due to the relatively recent definition of this type of risk. Considering this drawback, the employment of Bayesian methods and simulation tools could be a natural solution to the problem. The use of Bayesian methods allows us to integrate the scarce and, sometimes, inaccurate quantitative data collected by the bank with prior information provided by experts. An original proposal is a Bayesian approach for modelling operational risk and for calculating the capital required to cover the estimated risks. Besides this methodological innovation a computational scheme, based on Markov chain Monte Carlo simulations, is required. In particular, the application of the MCMC method to estimate the parameters of the marginals shows advantages in terms of a reduction of capital charge according to different choices of the marginal loss distributions.     

Communicating do-not-resuscitate orders with a computer-based system

BACKGROUND: Do-not-resuscitate (DNR) orders for critically ill patients are frequently miscommunicated between attending physicians, house staff, and nurses. A computer-based system was developed to improve the communication of a procedure-specific DNR order form. METHODS: Concordance of understanding of patients' DNR status was measured with the use of unstructured DNR orders (period 1), procedure-specific DNR order forms (period 2), and procedure-specific DNR order forms administered with a computer-based communication system (period 3). The 3 components of the DNR order assessed were (1) the clinical events to which the DNR order applied, (2) whether the DNR order withheld all elements of cardiopulmonary resuscitation, and (3) whether other treatments were to be withheld. RESULTS: For the 147 patients, the computer-based system in period 3 (n = 71) improved concordance for attending physicians and nurses or residents for all 3 of the DNR components compared with period 1 (n = 40) and some of the DNR components compared with period 2 (n = 36). Concordance was "substantial" or "almost perfect" as measured by the K statistic during period 3. The proportion of agreement for the composite of all 3 components of the DNR order increased during each period (P<.001, period 3 vs period 1). Overall agreement between all caregivers for the composite DNR order also improved from period 1 (22.2%) to period 2 (47.8%) and period 3 (61.9%; P<.001 vs period 1). Errors in order entry were detected by physicians because of the computer system and corrected in 9.9% of DNR orders in period 3. Progress note documentation of DNR status did not improve during period 3. The procedures of period 3 were considered acceptable by the physician and nursing staff. CONCLUSION: A computer-based system combined with a procedure-specific DNR order form improves communication of patients' DNR status in a critical care setting.     

Selective reduction of GluR2 protein in adult hippocampal CA3 neurons following status epilepticus but prior to cell loss

Kainic acid (KA) induces status epilepticus and delayed neurodegeneration of CA3 hippocampal neurons. Downregulation of glutamate receptor 2 (GluR2) subunit mRNA [the alpha-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) subunit that limits Ca2+ permeability] is thought to a play role in this neurodegeneration, possibly by increased formation of Ca2+ permeable AMPA receptors. The present study examined early hippocampal decreases in GluR2 mRNA and protein following kainate-induced status epilepticus and correlated expression changes with the appearance of dead or dying cells by several histological procedures. At 12 h, in situ hybridization followed by emulsion dipping showed nonuniform decreases in GluR2 mRNA hybridization grains overlying morphologically healthy-appearing CA3 neurons. GluR1 and N-methyl-D-aspartate receptor mRNAs were unchanged. At 12-16 h, when little argyrophilia or cells with some features of apoptosis were detected by silver impregnation or electron microscopy, single immunohistochemistry with GluR2 and GluR2/3 subunit-specific antibodies demonstrated a pattern of decreased GluR2 receptor protein within CA3 neurons that appeared to predict a pattern of damage, similar to the mRNA observations. Double immunolabeling showed that GluR2 immunofluorescence was depleted and that GluR1 immunofluorescence was sustained in clusters of the same CA3 neurons. Quantitation of Western blots showed increased GluR1:GluR2 ratios in CA3 but not in CA1 or dentate gyrus subfields. Findings indicate that the GluR1:GluR2 protein ratio is increased in a population of CA3 neurons prior to significant cell loss. Data are consistent with the "GluR2 hypothesis" that reduced expression of GluR2 subunits will increase formation of AMPA receptors permeable to Ca2+ and predict vulnerability to a particular subset of pyramidal neurons following status epilepticus.     

Infective endocarditis complicated with rapidly progressive glomerulonephritis: a case report

Despite a general national decline in criminal activities in the 1990s, juvenile criminal offenses continue to increase, (including violent, property, and delinquency acts). In addition increased numbers of children are being held in juvenile jails. It is all but impossible for pediatric health providers to think that "their patients" and "their practices" are immune from the epidemic of crime that affects and is caused by "just kids."     

Modeling the leadership – project performance relation: radial basis function,Gaussian and Kriging methods as alternatives to linear regression

The purpose of this paper is to analyze alternative forecasting methods that produce results at least similar to or better than linear regression (MLR) that can be used in the modeling of social systems. While organizations may be considered as typically non-linear systems, the common feature of most models found in literature continues to be the use of linear regression techniques. From a case study, advanced statistical methods of Gaussian and Kriging are evaluated, as well as an artificial intelligence (AI) tool, the radial basis function (RBF). The results show the best performance of the suggested methods compared to MLR, especially RBF, because of its uniform prediction behavior throughout all ranges of evaluation. These techniques, although somewhat unconventional in social systems modeling, present a potential contribution in increasing the accuracy and precision of the predictions allowing a more accurate assessment of the impact of certain strategies on the project performance to be made before the allocation of material, human and financial resources.