Total and CO-reactive heme content of actinorhizal nodules and the roots of some non-nodulated plants

The concentration of total and CO-reactive heme was measured in actinorhizal nodules from six different genera. This gave the upper limit to hemoglobin concentration in these nodules. Quantitative extraction of CO-reactive heme was achieved under anaerobic conditions in a buffer equilibrated with CO and containing Triton X-100. The concentration of CO-reactive heme in nodules of Casuarina and Myrica was approximately half of that found in legume nodules, whereas in Comptonia, Alnus and Ceanothus the concentrations of heme were about 10 times lower than in legume nodules. There was no detectable CO-reactive heme in Datisca nodules, but low concentrations were detected in roots of all non-nodulating plants examined, including Zea mays . Difference spectra of CO treated minus dithionite-reduced extracts displayed similar wavelengths of maximal and minimal light absorption for all extracts, and were consistent with those of a hemoglobin. The concentration of CO-reactive heme was not correlated to the degree to which CO inhibited nitrogenase activity nor was it affected by reducing the oxygen concentration in the rooting zone. However, there was a positive correlation between heme concentration and suberization or lignification of the walls of infected host cells. These observations demonstrate that, unlike legume nodules, high concentrations of heme or hemoglobin are not needed for active nitrogen fixation in most actinorhizal nodules. Nonetheless, a significant amount of CO-reactive heme is found in the nodules of Alnus, Comptonia, and Ceanothus, and in the roots of Zea mays . The identity and function of this heme is unknown.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43460/1/11104_2006_Article_BF02370943.pd

Respiration and oxygen transport in soybean nodules

The respiration rate of individual soybean ( Glycine max Merr.) nodules was measured as a function of pO 2 and temperature. At 23°, as the pO 2 was increased from 0.1 to 0.9 atm, there was a linear increase in respiration rate. At 13°, similar results were obtained, except that there was an abrupt saturation of respiration at approximately 0.5 atm pO 2 . When measurements were made on the same nodule, the rate of increase in respiration with pO 2 was the same at 13° and 23°. Additional results were that 5% CO in the gas phase had no effect on respiration, except for a small decrease in the pO 2 at which respiration became saturated. Also, nodules still attached to the soybean root displayed the same respiratory behavior as detached nodules. A model for oxygen transport in the nodule is presented which explains these results quantitatively. The essence of the model is that the respiration rate of the central tissue of the nodule is almost entirely determined by the rate of oxygen diffusion to the respiratory enzymes. Evidence is given that the nodule cortex is the site of almost all of the resistance to oxygen diffusion within the nodule.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47460/1/425_2004_Article_BF00388605.pd

Outcome Prediction of Postanoxic Coma:A Comparison of Automated Electroencephalography Analysis Methods

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest

Does Consideration and Assessment of Effects on Health Equity Affect the Conclusions of Systematic Reviews? A Methodology Study

INTRODUCTION: Tackling health inequities both within and between countries remains high on the agenda of international organizations including the World Health Organization and local, regional and national governments. Systematic reviews can be a useful tool to assess effects on equity in health status because they include studies conducted in a variety of settings and populations. This study aims to describe the extent to which the impacts of health interventions on equity in health status are considered in systematic reviews, describe methods used, and assess the implications of their equity related findings for policy, practice and research. METHODS: We conducted a methodology study of equity assessment in systematic reviews. Two independent reviewers extracted information on the reporting and analysis of impacts of health interventions on equity in health status in a group of 300 systematic reviews collected from all systematic reviews indexed in one month of MEDLINE, using a pre-tested data collection form. Any differences in data extraction were resolved by discussion. RESULTS: Of the 300 systematic reviews, 224 assessed the effectiveness of interventions on health outcomes. Of these 224 reviews, 29 systematic reviews assessed effects on equity in health status using subgroup analysis or targeted analyses of vulnerable populations. Of these, seven conducted subgroup analyses related to health equity which were reported in insufficient detail to judge their credibility. Of these 29 reviews, 18 described implications for policy and practice based on assessment of effects on health equity. CONCLUSION: The quality and completeness of reporting should be enhanced as a priority, because without this policymakers and practitioners will continue lack the evidence base they need to inform decision-making about health inequity. Furthermore, there is a need to develop methods to systematically consider impacts on equity in health status that is currently lacking in systematic reviews

Co-variations and Clustering of Chronic Disease Behavioral Risk Factors in China: China Chronic Disease and Risk Factor Surveillance, 2007

BACKGROUND: Chronic diseases have become the leading causes of mortality in China and related behavioral risk factors (BRFs) changed dramatically in past decades. We aimed to examine the prevalence, co-variations, clustering and the independent correlates of five BRFs at the national level. METHODOLOGY/PRINCIPAL FINDINGS: We used data from the 2007 China Chronic Disease and Risk Factor Surveillance, in which multistage clustering sampling was adopted to collect a nationally representative sample of 49,247 Chinese aged 15 to 69 years. We estimated the prevalence and clustering (mean number of BRFs) of five BRFs: tobacco use, excessive alcohol drinking, insufficient intake of vegetable and fruit, physical inactivity, and overweight or obesity. We conducted binary logistic regression models to examine the co-variations among five BRFs with adjustment of demographic and socioeconomic factors, chronic conditions and other BRFs. Ordinal logistic regression was constructed to investigate the independent associations between each covariate and the clustering of BRFs within individuals. Overall, 57.0% of Chinese population had at least two BRFs and the mean number of BRFs is 1.80 (95% confidence interval: 1.78-1.83). Eight of the ten pairs of bivariate associations between the five BRFs were found statistically significant. Chinese with older age, being a male, living in rural areas, having lower education level and lower yearly household income experienced increased likelihood of having more BRFs. CONCLUSIONS/SIGNIFICANCE: Current BRFs place the majority of Chinese aged 15 to 69 years at risk for the future development of chronic disease, which calls for urgent public health programs to reduce these risk factors. Prominent correlations between BRFs imply that a combined package of interventions targeting multiple BRFs might be appropriate. These interventions should target elder population, men, and rural residents, especially those with lower SES