Background and objectiveSerum D-dimer is elevated in respiratory disease. The objective of our study was to investigate the effect of D-dimer on in-hospital and 1-year mortality after acute exacerbations of chronic obstructive pulmonary disease (AECOPD).MethodsUpon admission, we measured 343 AECOPD patients’ serum D-dimer levels and arterial blood gas analysis, and recorded their clinical characteristics. The level of D-dimer that discriminated survivors and non-survivors was determined using a receiver operator curve (ROC). The risk factors for in-hospital mortality were identified through univariate analysis and multiple logistic regression analyses. To evaluate the predictive role of D-dimer for 1-year mortality, univariate and multivariate Cox regression analyses were performed.ResultsIn all, 28 patients died, and 315 patients survived in the in-hospital period. The group of dead patients had lower pH levels (7.35±0.11 vs 7.39±0.05, P<0.0001), higher D-dimer, arterial carbon dioxide tension (PaCO2), C-reactive protein (CRP), and blood urea nitrogen (BUN) levels (D-dimer 2,244.9±2,310.7 vs 768.2±1,078.4 µg/L, P<0.0001; PaCO2: 58.8±29.7 vs 46.1±27.0 mmHg, P=0.018; CRP: 81.5±66, P=0.001; BUN: 10.20±6.87 vs 6.15±3.15 mmol/L, P<0.0001), and lower hemoglobin levels (118.6±29.4 vs 128.3±18.2 g/L, P=0.001). The areas under the ROC curves of D-dimer for in-hospital death were 0.748 (95% confidence interval (CI): 0.641–0.854). D-dimer ≥985 ng/L was a risk factor for in-hospital mortality (relative risk =6.51; 95% CI 3.06–13.83). Multivariate logistic regression analysis also showed that D-dimer ≥985 ng/L and heart failure were independent risk factors for in-hospital mortality. Both univariate and multivariate Cox regression analyses showed that D-dimer ≥985 ng/L was an independent risk factor for 1-year death (hazard ratio (HR) 3.48, 95% CI 2.07–5.85 for the univariate analysis; and HR 1.96, 95% CI 1.05–3.65 for the multivariate analysis).ConclusionD-dimer was a strong and independent risk factor for in-hospital and 1-year death for AECOPD patients.

In this contribution, the 5,7-dibromo-8-hydroxyquinoline solubility in neat N,N-dimethylaniline (DMA), n-butanol, methanol, N,N-dimethylformamide (DMF), ethanol, 2-butanone, n-propanol, ethyl acetate, isopropanol, acetone, 1-methyl-2-pyrrolidinone (NMP), toluene, and 1,4-dioxolane was experimentally obtained using the shake-flask method covering a temperature range from 288.15 to 328.15 (333.15) K under ambient pressure of p = 101.2 kPa. The solubility values in the mole fraction scale of 5,7-dibromo-8-hydroxyquinoline in the studied thirteen solvents increased as the experimental temperature increased and ranked as NMP > DMA > DMF > 1,4-dioxane > ethyl acetate > toluene > acetone > 2-butanone > n-butanol > n-propanol > ethanol > isopropanol > methanol. The influence of interactions of solvent–solvent molecules and solvent–solute molecules on the 5,7-dibromo-8-hydroxyquinoline solubility was examined by the use of the linear solvation energy relationship according to the solvent descriptors. The obtained solubility data was correlated mathematically through the NRTL model, λh equation, Apelblat equation, and Wilson model. The acquired values of root-mean-square deviation and relative average deviation were no more than 23.53 × 10–4 and 5.18 × 10–2, respectively. In general, the values of relative average deviation attained through the Apelblat equation were smaller than that attained through the other models/equations for a certain neat solvent. The mixing property, infinitesimal concentration activity coefficient, and reduced excess enthalpy were resulted as well.

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Piezoelectric ceramics(PZT)actuator has been widely used in flexure-guided micro/nano positioning stage because of their high resolution. However, it is quite hard to achieve high-rate precision positioning control because of the complex hysteresis nonlinearity effect of PZT actuator. Thus, an online RELM algorithm with forgetting property (FReOS-ELM) is proposed to handle this issue. Firstly, we adopt regularized extreme learning machine (RELM)to build an intelligent hysteresis model. The training of the algorithm is completed only in one step, which avoids the shortcomings of the traditional hysteresis model based on artificial neural network (ANN) that slow training speed and easy to fall into the local minimum. Then, based on the regularized on-line sequential extreme learning machine (ReOS-ELM), an on-line RELM algorithm with forgetting property (FReOS-ELM) is designed, which can avoid the computational load of ReOS-ELM in the process of adding new data for learning on-line. In the experiment, a real-time voltage signal with varying frequencies and amplitudes is adopted, and the output displacement data of the micro/nano positioning stage is also acquired and analyzed. The experimental results show that the RELM-based hysteresis modeling algorithm has higher efficiency and more stable learning ability and generalization ability than the traditional neural network; In the aspect of online modeling, FReOS-ELM hysteresis modeling can achieve a better result than ReOS-ELM. Keywords: Piezoelectric ceramics • micro/nano positioning stage • flexure • hysteresis nonlinearity• extreme learning machine.

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