IntroductionThe use of prokinetic agents on post-pyloric placement of spiral nasojejunal tubes is controversial. The aim of the present study was to examine if metoclopramide or domperidone can increase the success rate of post-pyloric placement of spiral nasojejunal tubes.MethodsA multicenter, open-label, randomized, controlled trial was conducted in seven hospitals in China between April 2012 and February 2014. Patients admitted to the intensive care unit and requiring enteral nutrition for more than three days were randomly assigned to the metoclopramide, domperidone or control groups (1:1:1 ratio). The primary outcome was defined as the success rate of post-pyloric placement of spiral nasojejunal tubes, assessed 24 hours after initial placement. Secondary outcomes included success rate of post-D1, post-D2, post-D3 and proximal jejunum placement and tube migration distance. Safety of the study drugs and the tubes during the entire study period were recorded.ResultsIn total, 307 patients were allocated to the metoclopramide (n = 103), domperidone (n = 100) or control group (n = 104). The success rate of post-pyloric placement after 24 hours in the metoclopramide, domperidone and control groups was 55.0%, 51.5% and 27.3%, respectively (P = 0.0001). Logistic regression analysis identified the use of prokinetic agents, Acute Physiology and Chronic Health Evaluation (APACHE) II score <20, Sequential Organ Failure Assessment (SOFA) score <12 and without vasopressor as independent factors influencing the success rate of post-pyloric placement. No serious drug-related adverse reaction was observed.ConclusionsProkinetic agents, such as metoclopramide or domperidone, are effective at improving the success rate of post-pyloric placement of spiral nasojejunal tubes in critically ill patients.Trial registrationChinese Clinical Trial Registry ChiCTR-TRC-12001956. Registered 21 February 2012.

Because of the strong coupling between the prefractionator and the main distillation column involved, the dividing-wall distillation column (DWDC) may exhibit the so-called black-hole problem in the operating range of interest when four product compositions (i.e., the main compositions in the three products and the ratio between the two impurities in the intermediate product) have been specified. In this paper, a novel strategy is proposed to solve the black-hole problem by the arrangements of multiple intermediate products to the main distillation column of the DWDC. The number, locations, and flow rates of the multiple intermediate products are designated as decision variables to coordinate the relationship between the prefractionator and the main distillation column involved, and a simple procedure is developed for the determination of their values effectively. The separations of two ternary mixtures of hypothetical components, A, B, and C and ethanol, propanol, and butanol, are chosen as illustrative examples to evaluate the feasibility and effectiveness of the proposed strategy. In terms of steady-state analysis and dynamic operation studies, it is demonstrated that the black-hole problem can be completely circumvented by the arrangements of multiple intermediate products to the DWDC. The proposed strategy is considered to be of general significance and can be applicable and effective to the synthesis and design of the DWDC separating ternary mixtures with widely different thermodynamic properties.

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This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of Brain-Computer Interface (BCI). The novel kernel function has evolved from a chaotic system, which is inspired by the fact that human brain signals depict some chaotic characteristics and behaviors. By introducing the chaotic dynamics to the kernel function, the RVM will be enabled for higher classification capacity. The proposed method is validated within the framework of one versus one common spatial pattern (OVO-CSP) classifier to classify motor imagination (MI) of four movements in a public accessible dataset. To illustrate the performance of the proposed kernel function, Gaussian and Polynomial kernel functions are considered for comparison. Experimental results show that the proposed kernel function achieved higher accuracy than Gaussian and Polynomial kernel functions, which shows that the chaotic behavior consideration is helpful in the EEG signal classification.

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