Multistage Mechanical Activation of Multilayer Carbon Nanotubes in Creation of Electric Heaters with Self-Regulating Temperature

The article deals with research related to the issues of nanomodification of elastomers as a basis of electric heaters with self-regulating temperature. The effect of multistage mechanical activation of multilayer carbon nanotubes (MCNTs) with graphite on the uniformity of the temperature field distribution on the surface of nanomodified organosilicon elastomer has been studied. The influence of the stages of mechanical action on the parameters of MCNTs is revealed. It has been ascertained that for the MCNTs/graphite bulk material, which has passed the stage of mechanical activation in the vortex layer apparatus, a more uniform distribution of the temperature field and an increase in temperature to 57.1 °C at the supply voltage of 100 V are typical. The distribution of the temperature field in the centrifugal paddle mixer “WF-20B” for mixing MCNTs with graphite has been investigated. It has been found that there is also a thermal effect in addition to the mechanical action on the MCNTs in the paddle mixer “WF-20B”. The thermal effect is associated with the transfer of the mechanical energy of friction of the binary mixture MCNTs/graphite on the paddle and the walls of the vessel. The multiplicity of the starting current I_p to the nominal I_n (I_p/I_n) is 5 for the first sample, 7.5 for the second sample, and 10 for the third sample at the supply voltage of 100 V. The effect of reducing the starting current and stabilizing the temperature indicates the presence of self-regulation, which is expressed in maintaining a certain level of temperature.     

3种材质输液容器对药物的相容性及吸附性考察

目的:考察非聚氯乙烯(PVC)多层共挤复合膜输液袋、PVC输液袋和玻璃瓶与大输液的相容性及3种材质输液容器对6种药物的吸附性。方法:将5%葡萄糖注射液(5%GS)、0.9%氯化钠注射液(0.9%NaCl)分别置于3种材质输液容器中存放1y,观察、测定并比较5%GS、0.9%NaCl各质量控制指标及其变化;将一定量6种药品加入3种包装材质输液中,与输液混合均匀后分别于0、1、24h时测定药物浓度,并与0h时比较。结果:与玻璃瓶比较非PVC和PVC材质输液容器中5%GS、0.9%NaCl多数检测指标结果无显著性差异(P>0.05),但PVC材质中2种大输液含量下降(P<0.05);在PVC输液袋中,胰岛素、硝酸异山梨酯和硝酸甘油含量在34.44%～80.80%之间,含量下降百分比与玻璃瓶比较具有统计学差异(P<0.05);在玻璃瓶中,硝酸异山梨酯和硝酸甘油含量为72.83%～81.86%,含量下降百分比与非PVC比较具有统计学差异(P<0.05)。结论:PVC及玻璃瓶材质对部分药物有吸附性,应引起临床注意;非PVC材质与大输液相容性良好,对药物吸附性较小。     

非PVC多层共挤膜输液袋的研制及质量标准

目的 :研制非PVC多层共挤膜输液袋并制定其质量标准。方法 :将聚丙烯、聚乙烯、聚酰胺按一定比例共混 ,制备挤出膜后制成输液袋 ,按照大容量注射剂的要求考察并制定其质量标准。结果 :该输液袋质量优良 ,制作简单 ,不需配套昂贵的生产设备。结论 :非PVC多层共挤膜输液袋有进一步研究的价值     

新农合制度体系的问题及解决途径

目前农村医疗保障实行的新型农村合作医疗制度体系，是一种“强新型合作医疗＋弱医疗救助＋最弱卫生防疫”的三层次非均衡综合性制度框架结构。文章通过对其制度缺陷的分析，认为新农合制度体系很难保障广大农村居民对基本医疗卫生服务的需求，通过构建多层次农村医保模式，可以克服新农合制度体系的缺陷。     

武警内卫总队医院机动卫勤分队保障能力的多层次灰色分析

目的：健全武警内卫总队医院机动卫勤分队保障能力的评价方法．方法：依据武警内卫总队医院机动卫勤分队保障能力建设的要求，结合已确定的保障能力评估三级指标体系，运用多层次灰色分析方法，评价三所武警内卫总队医院机动卫勤分队的保障能力．结果：三所武警内卫总队医院机动卫勤分队的保障能力各不相同．结论：多层次灰色分析用熵的技术确定指标权重，避免了一定的主观性，评价方法直观可行，具有一定的实际意义．     

In vitro generation of a multilayered osteochondral construct with an osteochondral interface using rabbit bone marrow stromal cells and a silk peptide‐based scaffold

Tissue engineering of a biological osteochondral multilayered construct with a cartilage‐interface subchondral bone layer is a key challenge. This study presented a rabbit bone marrow stromal cell (BMSC)/silk fibroin scaffold‐based co‐culture approach to generate tissue‐engineered osteochondral grafts with an interface. BMSC‐seeded scaffolds were first cultured separately in osteogenic and chondrogenic stimulation media. The two differentiated pieces were then combined using an RADA self‐assembling peptide and subsequently co‐cultured. Gene expression, histological and biochemical analyses were used to evaluate the multilayered structure of the osteochondral graft. A complete osteochondral construct with a cartilage‐subchondral bone interface was regenerated and BMSCs were used as the only cell source for the osteochondral construct and interface regeneration. Furthermore, in the intermediate region of co‐cultured samples, hypertrophic chondrogenic gene markers type X collagen and MMP‐13 were found on both chondrogenic and osteogenic section edges after co‐culture. However, significant differences gene expression profile were found in distinct zones of the construct during co‐culture and the section in the intermediate region had significantly higher hypertrophic chondrocyte gene expression. Biochemical analyses and histology results further supported this observation. This study showed that specific stimulation from osteogenic and chondrogenic BMSCs affected each other in this co‐culture system and induced the formation of an osteochondral interface. Moreover, this system provided a possible approach for generating multilayered osteochondral constructs. Copyright © 2013 John Wiley & Sons, Ltd.     

Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale

Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria.     

Structured correlation in models for clustered data

Correlation is always a concern in the analysis of clustered data. One area of interest is to develop a general correlation modelling approach for high dimensional data with unbalanced hierarchical and heterogeneous data structures, e.g. multilevel data. Commonly used correlation structures might have limitation for such situations. In this paper, we propose two extensions, multiblock and multilayer correlations. These methods are very flexible in modelling correlation and can be incorporated in many multivariate approaches, while the major discussion focuses on the applications under the generalized estimating equations (GEE) methods. The approaches are especially useful in GEE when each cluster is large and complex but the number of clusters is small. If an incorrect correlation is applied to such data, the results are less efficient. Multiblock and multilayer correlations extend GEE methods to model complicated multilevel data with arbitrary number of levels and cluster size. The extended estimating equation for correlation parameters has an orthogonal property, and the computation is very efficient. A simulation study compares the conventional methods versus the proposed methods, and it shows the gain in relative efficiency and the flexibility in modelling various structures.     

Wavelet-Based Neural Network Analysis of Internal Carotid Arterial Doppler Signals

In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multilayer perceptron neural network (MLPNN) trained with the Levenberg–Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg–Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion.