Prediction of outcomes in hypertensive patients with suspected coronary disease

Stress echocardiography has been shown to improve the diagnosis of coronary artery disease in the presence of hypertension, but its value in prognostic evaluation is unclear. We sought to determine whether stress echocardiography could be used to predict mortality in 2363 patients with hypertension, who were followed for up to 10 years (mean 4.0+/-1.8) for death and revascularization. Stress echocardiograms were normal in 1483 patients (63%), 16% had resting left ventricular (LV) dysfunction alone, and 21% had ischemia. Abnormalities were confined to one territory in 489 patients (21%) and to multiple territories in 365 patients (15%). Cardiac death was less frequent among the patients able to exercise than among those undergoing dobutamine echocardiography (4% versus 7%, P<0.001). The risk of death in patients with a negative stress echocardiogram was <1% per year. Ischemia identified by stress echocardiography was an independent predictor of mortality in those able to exercise (hazard ratio 2.21, 95% confidence intervals 1.10 to 4.43, P=0.0001) as well as those undergoing dobutamine echo (hazard ratio 2.39, 95% confidence intervals 1.53 to 3.75, P=0.0001); other predictors were age, heart failure, resting LV dysfunction, and the Duke treadmill score. In stepwise models replicating the sequence of clinical evaluation, the results of stress echocardiography added prognostic power to models based on clinical and stress-testing variables. Thus, the results of stress echocardiography are an independent predictor of cardiac death in hypertensive patients with known or suspected coronary artery disease, incremental to clinical risks and exercise results.

AUSLEM (AUStralian Land Erodibility Model): A tool for identifying wind erosion hazard in Australia

We present AUSLEM (AUStralian Land Erodibility Model), a land erodibility modelling system that utilizes a rule-set of surficial and climatic thresholds applied through a Geographic Information System (GIs) modelling framework to predict landscape susceptibility to wind erosion. AUSLEM is distinctive in that it quantitatively assesses landscape susceptibility to wind erosion at a 5 x 5 km. spatial resolution on a monthly time-step across Australia. The system was implemented for representative wet (1984), dry (1994), and average rainfall (1997) years with corresponding low, high and moderate dust storm day frequencies. Results demonstrate that AUSLEM can identify landscape erodibility, and provide an interpretation of the physical nature and distribution of erodible landscapes in Australia. Further, results offer an assessment of the dynamic tendencies of erodibility in space and time in response to the El Nino Southern Oscillation (ENSO) and seasonal synoptic scale climate variability. A comparative analysis of AUSLEM output with independent national and international wind erosion, atmospheric aerosol and dust event records indicates a high level of model competency. (c) 2006 Elsevier B.V. All rights reserved.

The prediction of disease risk in genomic medicine

In recent years, the phrase 'genomic medicine' has increasingly been used to describe a new development in medicine that holds great promise for human health. This new approach to health care uses the knowledge of an individual's genetic make-up to identify those that are at a higher risk of developing certain diseases and to intervene at an earlier stage to prevent these diseases. Identifying genes that are involved in disease aetiology will provide researchers with tools to develop better treatments and cures. A major role within this field is attributed to 'predictive genomic medicine', which proposes screening healthy individuals to identify those who carry alleles that increase their susceptibility to common diseases, such as cancers and heart disease. Physicians could then intervene even before the disease manifests and advise individuals with a higher genetic risk to change their behaviour - for instance, to exercise or to eat a healthier diet - or offer drugs or other medical treatment to reduce their chances of developing these diseases. These promises have fallen on fertile ground among politicians, health-care providers and the general public, particularly in light of the increasing costs of health care in developed societies. Various countries have established databases on the DNA and health information of whole populations as a first step towards genomic medicine. Biomedical research has also identified a large number of genes that could be used to predict someone's risk of developing a certain disorder. But it would be premature to assume that genomic medicine will soon become reality, as many problems remain to be solved. Our knowledge about most disease genes and their roles is far from sufficient to make reliable predictions about a patient’s risk of actually developing a disease. In addition, genomic medicine will create new political, social, ethical and economic challenges that will have to be addressed in the near future.

Prediction of the chemical composition of lamb carcasses from multi-frequency impedance data

Multi-frequency bioimpedance analysis (MFBIA) was used to determine the impedance, reactance and resistance of 103 lamb carcasses (17.1-34.2 kg) immediately after slaughter and evisceration. Carcasses were halved, frozen and one half subsequently homogenized and analysed for water, crude protein and fat content. Three measures of carcass length were obtained. Diagonal length between the electrodes (right side biceps femoris to left side of neck) explained a greater proportion of the variance in water mass than did estimates of spinal length and was selected for use in the index L-2/Z to predict the mass of chemical components in the carcass. Use of impedance (Z) measured at the characteristic frequency (Z(c)) instead of 50 kHz (Z(50)) did not improve the power of the model to predict the mass of water, protein or fat in the carcass. While L-2/Z(50) explained a significant proportion of variation in the masses of body water (r(2) 0.64), protein (r(2) 0.34) and fat (r(2) 0.35), its inclusion in multi-variate indices offered small or no increases in predictive capacity when hot carcass weight (HCW) and a measure of rib fat-depth (GR) were present in the model. Optimized equations were able to account for 65-90 % of the variance observed in the weight of chemical components in the carcass. It is concluded that single frequency impedance data do not provide better prediction of carcass composition than can be obtained from measures of HCW and GR. Indices of intracellular water mass derived from impedance at zero frequency and the characteristic frequency explained a similar proportion of the variance in carcass protein mass as did the index L-2/Z(50).

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Self-efficacy for refusal mediated by outcome expectancies in the prediction of alcohol-dependence amongst young adults

The present study examined the relative importance of outcome expectancies and self-efficacy [1] in the prediction of alcohol dependence [2] and alcohol consumption in a sample of young adult drinkers drawn from a milieu previously reported as supportive of risky drinking. In predicting alcohol dependence, outcome expectancies were found to mediate self-efficacy and the same pattern was found for both males and females. This suggests that male and female drinkers may become more similar as they progress along the drinking continuum from risky drinking to dependent drinking. However, in women, in comparison to men, a greater array of expectancies and self-efficacy scales were found to predict heavy drinking, as measured by quantity and frequency. These results suggest that heavy drinking women are particularly at risk of developing drinking related complications and that preventative education needs to take into account gender differences.

El Nino and arboviral disease prediction

Recent El Nino events have stimulated interest in the development of modeling techniques to forecast extremes of climate and related health events. Previous studies have documented associations between specific climate variables (particularly temperature and rainfall) and outbreaks of arboviral disease. In some countries, such diseases are sensitive to Fl Nino. Here we describe a climate-based model for the prediction of Ross River virus epidemics in Australia. From a literature search and data on case notifications, we determined in which years there were epidemics of Ross River virus in southern Australia between 1928 and 1998. Predictor variables were monthly Southern Oscillation index values for the year of an epidemic or lagged by 1 year. We found that in southeastern states, epidemic years were well predicted by monthly Southern Oscillation index values in January and September in the previous year. The model forecasts that there is a high probability of epidemic Ross River virus in the southern states of Australia in 1999. We conclude that epidemics of arboviral disease can, at least in principle, be predicted on the basis of climate relationships.

Evaluation of a new bioelectrical impedance instrument for the prediction of body cell mass independently of height or weight

The objective of the present study was to evaluate the performance of a new bioelectrical impedance instrument, the Soft Tissue Analyzer (STA), which predicts a subject's body composition. A cross-sectional population study in which the impedance of 205 healthy adult subjects was measured using the STA. Extracellular water (ECW) volume (as a percentage of total body water, TBW) and fat-free mass (FFM) were predicted by both the STA and a compartmental model, and compared according to correlation and limits of agreement analysis, with the equivalent data obtained by independent reference methods of measurement (TBW measured by D2O dilution, and FFM measured by dual-energy X-ray absorptiometry). There was a small (2.0 kg) but significant (P < 0.02) difference in mean FFM predicted by the STA, compared with the reference technique in the males, but not in the females (-0.4 kg) or in the combined group (0.8 kg). Both methods were highly correlated. Similarly, small but significant differences for predicted mean ECW volume were observed. The limits of agreement for FFM and ECW were -7.5-9.9 and -4.1-3.0 kg, respectively. Both FFM and ECW (as a percentage of TBW) are well predicted by the STA on a population basis, but the magnitude of the limits of agreement with reference methods may preclude its usefulness for predicting body composition in an individual. In addition, the theoretical basis of an impedance method that does not include a measure of conductor length requires further validation. (C) Elsevier Science Inc. 2000.

Prediction of rock alteration patterns: a potential tool in mineral exploration

Hydrothermal alteration of a quartz-K-feldspar rock is simulated numerically by coupling fluid flow and chemical reactions. Introduction of CO2 gas generates an acidic fluid and produces secondary quartz, muscovite and/or pyrophyllite at constant temperature and pressure of 300 degrees C and 200 MPa. The precipitation and/or dissolution of the secondary minerals is controlled by either mass-action relations or rate laws. In our simulations the mass of the primary elements are conserved and the mass-balance equations are solved sequentially using an implicit scheme in a finite-element code. The pore-fluid velocity is assumed to be constant. The change of rock volume due to the dissolution or precipitation of the minerals, which is directly related to their molar volume, is taken into account. Feedback into the rock porosity and the reaction rates is included in the model. The model produces zones of pyrophyllite quartz and muscovite due to the dissolution of K-feldspar. Our model simulates, in a simplified way, the acid-induced alteration assemblages observed in various guises in many significant mineral deposits. The particular aluminosilicate minerals produced in these experiments are associated with the gold deposits of the Witwatersrand Basin.

The role of cognitive vulnerability and stress in the prediction of postpartum depressive symptomatology

Objectives. The present study was designed to test the diathesis-stress components of Beck's cognitive theory of depression and the reformulated learned helplessness model of depression in the prediction of postpartum depressive symptomatology. Design and methods. The research used a two-wave longitudinal design-data were collected from 65 primiparous women during their third trimester of pregnancy and then 6 weeks after the birth. Cognitive vulnerability and initial depressive symptomatology were assessed at Time 1, whereas stress and postpartum depressive symptomatology were assessed at Time 2. Results. There was some support for the diathesis-stress component of Beck's cognitive theory, to the extent that the negative relationship between both general and maternal-specific dysfunctional attitudes associated with performance evaluation and Time 2 depressive symptomatology was strongest for women who reported high levels of parental stress. In a similar vein, the effects of dysfunctional attitudes (general and maternal-specific) associated with performance evaluation and need for approval (general measure only) on partner ratings of emotional distress were evident only among those women whose infants were rated as being temperamentally difficult. Conclusion. There was no support for the diathesis-stress component of the reformulated learned helplessness model of depression; however, there was some support for the diathesis-stress component of Beck's cognitive theory.

Prediction of promiscuous peptides that bind HLA class I molecules

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, multipred, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. multipred is both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. multipred replaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that multipred can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.