To evaluate the absorptive characteristics of furfural onto biomass charcoals derived from rice husk pyrolysis, we studied the information of the structure and surface chemistry properties of the rice husk charcoals modified by thermal treatment under nitrogen and carbon dioxide flow and adsorption mechanism of furfural. The modified samples are labeled as RH-N2 and RH-CO2. Fresh rice husk charcoal sample (RH-450) and modified samples were characterized by elemental analysis, nitrogen adsorption-desorption isotherms, Fourier-transform infrared spectroscopy and Boehm titration. The results show that fresh rice husk charcoal obtained at 450 degrees C had a large number of organic groups on its surface and poor pore structure. After the modification under nitrogen and carbon dioxide flow, oxygenic organics in rice husk charcoals decompose further, leading to the reduction of acidic functional groups on charcoals surface, and the increase of the pyrone structures of the basic groups. Meanwhile, pore structure was improved significantly and the surface area was increased, especially for the micropores. This resulted in the increase of π-π dispersion between the surfaces of rice husk charcoals and furfural molecular. With making comprehensive consideration of π-π dispersion and pore structure, the best removal efficiency of furfural was obtained by rice husk charcoal modified under carbon dioxide flow.
Adsorption ; Biomass ; Carbon Dioxide ; Charcoal ; Furaldehyde ; chemistry ; Nitrogen ; Oryza ; Spectroscopy, Fourier Transform Infrared ; Surface Properties

Objective To evaluate the effect of using"metronome"to guide chest compression in the new nurse training of cardiopulmonary resuscitation(CPR). Methods From 2015 to 2016,385 new nurses were enrolled and randomly by random number table divided into four groups based on Solomon four-group design:control group 1(96 nurses),sudy group 1(89 nurses),control group 2(104 nurses) and study group 2(96 nurses),Control group 1 and control group 2 accepted normal training plus "chest compressions operation formula" training,study group 1 and study group 2 got "metronome" guiding based on the frequency training.Control group 1 and study group 1 took a baseline test before the training started,while control group 2 and study group 2 didn't.Four groups took part in an operational test after the seven days of training. Results There was no statistical difference between the baseline test scores of control group 1 and study group 1(P>0.05).After the training,the score of study group 1 was 93.4±3.5, which was significantly higher than 91.6 ± 4.7 of control group 1(t=-2.870, P<0.01).Through covariance analysis,after removing the impact of the baseline test score,the score of study group was statistically significant higher than that of control group 1 (F=7.33, P=0.007).Furthermore,there was no statistically significant interaction between the baseline test score and training method (P>0.05).Meanwhile,after training,study group 2 scores was 93.5±3.5,which was statistically higher than 92.1±4.0 of control group 2(t=2.670,P=0.008).Control group 1 were combined with control 2 to form control group,as well as study group 1 with study group 2 to form study group.A comparison between these two new groups indicates that the new study group surpasses the new control group with lower error frequencies in the position,depth and frequency of external cardiac compression (t=4.701 8, 12.488 9, 11.993 9, P<0.05).In addition,the study group also showed a better performance, when taking into consideration of the region and skills of compression and the observation of complexion,while there was no statistical difference between the error frequencies in these three aspects of these two new groups(P>0.05). Conclusions In the CPR training of new nurses,using a 100 times/min"metronome"can help new nurses grasp the accurate compression rates and depth, improve the overall quality of this procedure,making it a simple and effective training method,which is worthy of popularization.

ObjectiveTo explore the efficacy-driving mechanism of Danhong injection （DHI） in the treatment of stable angina pectoris （SAP） based on the composition-activity relationship of target modules and clarify the pharmacological effects of DHI. MethodAccording to the angina frequency （AF） in the Seattle Angina Questionnaire （SAQ） that was obtained in the previous clinical trial， the patients before and after DHI treatment were grouped based on efficacy. The transcriptomic data of the patients before treatment and in the best efficacy group 30 days post-treatment were selected as the data source， and then weighted gene co-expression network analysis （WGCNA） was employed to construct the co-expression network. Relevant modules in the network were identified and associated with clinical features. In addition， the On-modules （Z value below 0） were identified by Zsummary. The topological indicators such as density， centrality， and clustering coefficient were adopted to explore the dynamics of DHI efficacy at the network level and module level， respectively. In addition， the driver genes were screened by the personalized network control （PNC） algorithm. Finally， rat H9C2 cells were used to establish the model of hypoxia/reoxygenation （H/R）， which was used to confirm the potential therapeutic target of DHI for SAP and provide a scientific basis for revealing the therapeutic mechanism of DHI. ResultWe identified 19 modules in the best efficacy group of DHI for SAP， and the comparison between day 0 and day 30 revealed 12 On-modules. The changes of network topological indicators at the network and module levels confirmed the correlation between the best efficacy of DHI treatment and topological dynamics. Finally， the driver genes， Klotho and fibroblast growth factor 22 （FGF22）， in DHI treatment of SAP were verified by the H9C2 cell model of H/R. ConclusionBased on clinical transcriptome data， this study determined the composition-activity relationship of target modules of DHI for SAP， which provided a scientific basis for deciphering the efficacy-driven mechanism of DHI for SAP.