Prediction of gold stage in patients hospitalized with COPD exacerbations using blood neutrophils and demographic parameters as risk factors.

BACKGROUND: Patients hospitalized with chronic obstructive pulmonary disease (COPD) exacerbations are unable to complete the pulmonary function test reliably due to their poor health conditions. Creating an easy-to-use instrument to identify the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage will offer valuable information that assists clinicians to choose appropriate clinical care to decrease the mortality in these patients. The objective of this study was to develop a prediction model to identify the GOLD stage in the hospitalized exacerbation of chronic obstructive pulmonary disease (ECOPD) patients. METHODS: This prospective study involved 155 patients hospitalized for ECOPD. All participants completed lung function tests and the collection of blood neutrophils and demographic parameters. Receiver operating characteristic (ROC) curve was plotted based on the data of 155 patients, and was used to analyze the disease severity predictive capability of blood neutrophils and demographic parameters. A support vector regression (SVR) based GOLD stage prediction model was built using the training data set (75%), whose accuracy was then verified by the testing data set (25%). RESULTS: The percentage of blood neutrophils (denoted as NEU%) combined with the demographic parameters was associated with a higher risk to severe episode of ECOPD. The area under the ROC curve was 0.84. The SVR model managed to predict the GOLD stage with an accuracy of 90.24%. The root-mean-square error (RMSE) of the forced expiratory volume in one second as the percentage of the predicted value (denoted as FEV1%pred) was 8.84%. CONCLUSIONS: The NEU% and demographic parameters are associated with the pulmonary function of the hospitalized ECOPD patients. The established prediction model could assist clinicians in diagnosing GOLD stage and planning appropriate clinical care.

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.

Prediction models for pulmonary function during acute exacerbation of chronic obstructive pulmonary disease.

OBJECTIVE: The pulmonary function test is an effort-dependent test; however, during acute exacerbation of chronic obstructive pulmonary disease (AECOPD), patients are unable to effectively cooperate due to poor health. The present study aimed to establish prediction models that only require demographic and inflammatory parameters to predict pulmonary function indexes: forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). APPROACH: The goal was to establish prediction models based on multi-output support vector regression. A total of 143 subjects received a peripheral blood examination and pulmonary function test. The demographic and inflammatory parameters were used as input features, and FEV1 and FVC were used as the target features in prediction models. Three models (mixed model, severe model and nonsevere model) were established with FEV1 < 1 l as the threshold of severe episodes of AECOPD. The values of FEV1 and FVC from the pulmonary function tests were compared with the prediction models to validate the performances of the developed prediction models. MAIN RESULTS: The severe and nonsevere models' prediction performances were better than that of the mixed model. The mean squared errors were lower than 0.05 l2, and the decision coefficients (R 2) were higher than 0.40. The two-tailed t-test results showed that for both severe and nonsevere models, the absolute percentage errors of FEV1 and FVC were within 10%. SIGNIFICANCE: Our study shows the feasibility of predicting the pulmonary function indexes FEV1 and FVC with demographic and inflammatory parameters when the pulmonary function test fails to be implemented, which is beneficial for the treatment of AECOPD.

COP9 signalosome subunit Csn8 is involved in maintaining proper duration of the G1 phase.

The COP9 signalosome (CSN) is a conserved protein complex known to be involved in developmental processes of eukaryotic organisms. Genetic disruption of a CSN gene causes arrest during early embryonic development in mice. The Csn8 subunit is the smallest and the least conserved subunit, being absent from the CSN complex of several fungal species. Nevertheless, Csn8 is an integral component of the CSN complex in higher eukaryotes, where it is essential for life. By characterizing the mouse embryonic fibroblasts (MEFs) that express Csn8 at a low level, we found that Csn8 plays an important role in maintaining the proper duration of the G1 phase of the cell cycle. A decreased level of Csn8, either in Csn8 hypomorphic MEFs or following siRNA-mediated knockdown in HeLa cells, accelerated cell growth rate. Csn8 hypomorphic MEFs exhibited a shortened G1 duration and affected expression of G1 regulators. In contrast to Csn8, down-regulation of Csn5 impaired cell proliferation. Csn5 proteins were found both as a component of the CSN complex and outside of CSN (Csn5-f), and the amount of Csn5-f relative to CSN was increased in the Csn8 hypomorphic cells. We conclude that CSN harbors both positive and negative regulators of the cell cycle and therefore is poised to influence the fate of a cell at the crossroad of cell division, differentiation, and senescence.

[Excitation-emission fluorescence characterization study of the three phenolic compounds].

The fluorescence intensity information was collected by scanning its fluorescence spectra at different excitation wavelengths. Based on its high sensitivity and selectivity, excitation-emission fluorescence can be widely used for detection of pollutants in the environment. The characterizations of the three phenolic compounds were investigated by this method, and the fluorescence peaks of phenol, m-cresol and thymol were confirmed at 272/300 nm, 274/300 nm and 276/304 nm when the excitation and emission wavelength were set in the range of 240-360 nm and 260-500 nm respectively. The excitation and emission spectra of the three phenolic compounds are very similar because of their analogical structure. The intensity of the spectrum has a good linear relationship with the concentration when the solution concentration is between 0.02 and 1.0 mg x L(-1), and the limits of detection can reach at 1 microg x L(-1). Results show that the three compounds can be analyzed qualitatively and quantitatively by excitation-emission fluorescence.