垂直和平行方向的骨骼肌介电谱的比较研究

在10Hz-100MHz频率范围内，应用阻抗分析仪测量了骨骼肌纤维走行与测量电场呈垂直和平行两个方向上的蛙离体骨骼肌的介电谱。比较了平行和垂直于外施电场方向的骨骼肌细胞介电谱的区别：（1）在低频低，平行方向电导率（K1，∥）高于垂直方向电导率（K1，⊥）近10倍；（2）在高频段，平行方向电导率（Kh,∥）与垂直方向电导率（Kh,⊥）不相等，存在着约0.8mS/cm的差值；（3）在电导率Cole-Cole图上，平行方向电导率增量（△k∥)低于垂直方向电导率增量（△k⊥);(4)在介电损失（ε“)方面，平行方向介电损失（ε“∥）主峰明显高于垂直方向介电损失（ε“⊥)主峰；（5）在介电损耗角正切（tgδ）方面，平行介电损耗正切（tgδ∥）主峰的频率滞后于垂直方向介电损耗角正切（tgδ⊥）主峰的频率，但是tgδ∥峰值高于tgδ⊥峰值。     

应用Cole-Cole公式分析蛙血液细胞介电谱

利用Cole-Cole公式对10^4～10^8Hz频率段的蛙血液细胞介电谱进行了曲线拟合分析，建立了蛙血液细胞Cole-Cole参数：高频段相对介电常数εh=60，第一相对介电增量△εl=4900，第二相对介电增量△ε2=1200，第一特征频率fl=0．2MHz，第二特征频率f2=2．4MHz，第一相位角βl=0．92，第二相位角β2=0．93，低频段电导率εl=3．38ms／cm。     

蛙骨骼肌细胞的被动电特性

应用宽频域阻抗测量技术研究骨骼肌纤维走行与电场呈垂直和平行两个方向的蛙离体骨骼肌的被动电特性。采用Cole-Cole数学模型的非线性数值计算,经过频域介电谱、Cole-Cole图、介电损耗因子和介电损耗角正切频率谱的曲线拟合分析,建立了蛙骨骼肌细胞在垂直和平行两个方向的介电参数：在垂直方向,高频段介电常数εh=78,第一介电常数增量△ε1=113000,第二介电常数增量△ε2=45000,第一特征频率,fc1=9kHz,第二特征频率fc2=158kHz,第一相位角β1=0．881,第二相位角β2=0．984,低频段电导率κL=0．55mS／cm;在平行方向,εh=78,△ε1=550000,△ε2=140000,fc1=1．12kHz,fc2=19．9kHz,β1=0．92,β2=0．99,κL=4．5mS／cm。     

细胞比容对人全血细胞介电谱的影响

在10kHz～100MHz频率范围内，通过阻抗分析仪观察了血细胞比容对人全血细胞介电谱和Cole-Cole图的影响。结果表明：随着血细胞比容CT的增加，低频段介电常数εL和介电增量△ε增加；低频段电导率κL和高频率段电导率κh减小。电导率增量△κ增加；介电常数Cole-Cole图的半径、面积、右截距增加，圆心右移；反之，电导率Cole-Cole图的半径、面积、左右截距减少，圆心左移。     

频域阻抗法研究细胞介电特性

对生物细胞介电特性的研究是生物医学物理学公认的前沿重点课题之一.本文介绍如何采用宽频域交流阻抗技术研究细胞介电特性的原理和方法,以及在细胞介电谱的基础上,如何通过非线性数值计算建立细胞的数学模型和物理模型.本文以蛙骨骼肌细胞为例,在100Hz～100 MHz范围内,应用阻抗分析仪测量了细胞交流阻抗,通过细胞介电谱、Cole-Cole图、介电耗损因子频率谱、介电损耗角正切频率谱等方式,分析了骨骼肌细胞介电特性的数据特征;最后使用Cole-Cole模型和椭圆壳介电理论,建立了骨骼肌细胞的数学模型和物理模型,为今后利用数理模型方法分析骨骼肌疲劳、骨骼肌萎缩等奠定了基础.     

血液电特性的研究进展

血液组成结构的特点决定了血液的电特性,电特性的参数主要是电导率和介电常数.本文从红细胞流变性、血液结构,以及抗凝剂、存储时间和频率等环境因素方面,对血液的电学特性进行综述.     

人血液细胞介电谱的实验研究

在0．01～100MHz范围内，测量人血液细胞的介电谱，确立人血液细胞对交流电场的介电响应的数据特征。利用频域阻抗技术测量了正常人血液细胞交流阻抗，绘制细胞的介电常数和电导率与电场频率的关系曲线。建立了人血液细胞的介电谱和Cole—Cole图，明确了人血细胞的介电频响的数据特征。在射频电场中，人血液细胞的介电常数和电导率具有频率依赖性，表现为具有两个特征频率的介电弛豫：第一介电弛豫发生在fc。为1．42MHz，第二介电弛豫产生在fcz为3．32MHz。     

细胞外灌流对骨骼肌细胞介电谱的影响

通过灌流的方法,探讨细胞外液对骨骼肌细胞介电谱的影响,应用非线性Co le-Co le公式的曲线拟合与数值计算,比较灌流后骨骼肌细胞介电谱参数,结果表明:(1)低频段介电常数Lε和高频段电导率hκ随着细胞外灌流液电导率的降低而减少;(2)细胞外灌流对骨骼肌细胞介电谱的特征频率(fC 1,fC 2)影响不大;(3)细胞外灌流主要影响骨骼肌细胞Δε″和tgδ的峰值。     

正常人血小板介电谱的实验研究

在100KHz～110MHz范围内,测量人血小板的介电谱,分析了人血小板对交流电场的介电响应的数据特征。利用频域阻抗技术首次测量了正常人血小板交流阻抗,绘制血小板的介电常数和电导率与电场频率的关系曲线。建立了人血小板的介电谱和Cole-Cole图,明确了人血小板的介电频响的数据特征。在射频电场中,人血小板的介电常数和电导率具有频率依赖性,血小板介电谱具有两个特征频率:第一特征频率fC1为6.66MHz,第二特征频率fC2为9.81MHz。     

骨骼肌细胞的介电弛豫特性

在100Hz～100MHz频率范围内,利用非线性数值计算和曲线拟合分析,验证了蛙离体骨骼肌细胞的介电弛豫特性满足Cole-Cole公式(误差≤3.45%),通过频域介电谱、Cole-Cole图、介电损耗因子和介电损耗角正切频率谱的曲线拟合分析,确定了蛙骨骼肌细胞的Cole-Cole介电参数:高频段相对介电常数εh=78,第一相对介电增量△ε1=113000,第二相对介电增量△ε2=45000,第一特征频率fc1=9 kHz,第二特征频率fc2=158 kHz,第一相位角β1=0.881,第二相位角β2=0.984,低频段电导率κL=0.55mS/cm,常数A=35,常数m=1.08.     

Statistical analysis of the accuracy of water content‐based electrical properties tomography

Water content‐based electrical properties tomography (wEPT) can retrieve electrical properties (EPs) from water content maps, thereby eliminating the need for B₁ field measurement in the traditional magnetic resonance electrical properties tomography method. The wEPT is performed by conventional MR scanning, such as T₁ ‐weighted spin‐echo imaging, and thus can be directly applied to clinical settings. However, the random noise propagation involved in wEPT causes inaccuracy in EP mapping. To guarantee the EP estimates desired for clinical practice, this study statically investigates the noise‐specific uncertainty of wEPT through probability density function models. We calculated the probability distribution of EP maps with different noise levels and examined the effects of scan parameters on reconstruction accuracy with various flip angles (FAs) and repetition time (TR) settings. The theoretical derivation was validated by Monte Carlo simulations and human imaging experiment at 3 T. Results showed that a serious deviation could occur in tissues with large conductivity value at a low signal‐to‐noise ratio and quantitatively demonstrate that such deviation could be mitigated by increased FAs or TRs. This study provided useful information for the setup of scan parameters, evaluation of accuracy of the wEPT under specific SNR levels, and promote its clinical applications.     

0.1 MHz～100 MHz大鼠血液细胞的介电响应

目的：确立正常大鼠血液细胞对0．1MHz-100MHz交流电场的介电响应的数据特征。材料与方法：取10只雄性Sprague-Dawley大鼠全血，利用Agilent4294A阻抗分析仪测量了26个全血细胞悬浮液样本的交流阻抗，通过频域介电谱和Cole-Cole图的数据分析，确立了正常大鼠血液细胞对交流电场的介电响应的数据特征。结果：在0．1MHz-100MHz频率范围内，大鼠血液细胞的介电常数和电导率具有电场频率的依赖关系，主要表现在具有两个中心特征频率的介电弛豫：第一介电弛豫约发生在fc1=2MHz，第二介电弛豫约产生在fc2=3MHz。结论：交流阻抗技术可以观察血液细胞的介电响应特性。     

Quantitative analysis of the reconstruction errors of the currently popular algorithm of magnetic resonance electrical property tomography at the interfaces of adjacent tissues

This work quantitatively analyzed the reconstruction errors (REs) of electrical property (EP) images using a currently popular algorithm of magnetic resonance electrical property tomography (MREPT), which occurred along the tissue interfaces. Transmitted magnetic fields were acquired at 3 T using a birdcage coil loaded with a phantom consisting of various adjacent tissues. Homogeneous Helmholtz was employed to calculate the EP maps by Laplacian computation of central differences. The maps of absolute REs (aREs) and relative REs (rREs) were calculated. The maximum and mean rREs, in addition to rRE distributions at the interfaces, were presented. Reconstructed EP maps showed various REs along different interface boundaries. Among all the investigated tissue interfaces, the kidney–fat interface presented the maximum mean rREs for both conductivity and relative permittivity. The minimum mean rRE of conductivity was observed at the spleen–muscle interface, and the minimum mean rRE of relative permittivity was detected along the lung–heart interface. The mean rREs ranged from 0.3986 to 36.11 for conductivity and 0.2218 to 11.96 for relative permittivity. Overall, this research indicates that different REs occur at various tissue boundaries, as shown by the currently popular algorithm of MREPT. Thus, REs should be considered when applying MREPT to reconstruct the EP distributions inside the human body. Copyright © 2016 John Wiley & Sons, Ltd.     

Critical role of peripheral blood phagocytes and the involvement of complement in tumour necrosis factor enhancement of passive collagen-arthritis.

Studies have implicated tumour necrosis factor-alpha (TNF-alpha) in type-II collagen (CII)-induced arthritis (CIA), a well established animal model of human rheumatoid arthritis. Precisely how TNF is involved in CIA is not yet clear. In this study the effects of TNF on CIA were examined, independent of its potential effects on the immune response, by performing peri-articular injection of TNF in combination with passive immunization of rats. A sub-arthritic dose (5 mg) of affinity-purified anti-CII IgG, which alone was insufficient to induce spontaneous clinical arthritis, was used throughout the study. Obvious clinical arthritis that persisted for several days was rapidly induced by injections of 100 ng TNF into hindpaws of rats that were passively immunized shortly before the TNF injection. Injections of TNF in non-immunized control rats did not induce clinical arthritis, nor did buffer-only injections in passively immunized controls. The clinical arthritic response was a local phenomenon, limited only to the TNF-injected hindpaws. No swelling was observed in the opposite, buffer-injected hindpaws, indicating the effects of TNF were not systemic. Depletion of peripheral blood phagocytes with anti-rat neutrophil antiserum before passive immunization completely abolished the ability of TNF to induce clinical arthritis, identifying phagocytic cells as the essential target cells in evoking this arthritic response. A role for complement activation was also demonstrated in this model through the use of a soluble recombinant version of CD35, the cell surface complement receptor type-1 (sCR1, BRL55730), which significantly reduced TNF-induced arthritis in phagocyte-replete rats.     

血液电阻抗三参数的计算方法

根据血液电阻抗的三参数表达式,选取双频率复阻抗信息作为已知量,详细介绍了三参数值的推导过程,建立了一套可获得三参数基础量和博动量计算方法。     

生理信号时问序列周期性和平稳性对近似熵和样本熵算法的影响分析

目的提高近似熵和样本熵算法在评价生理信号时间序列非线性复杂度应用中的精度。方法首先生成生理信号时间序列数据库,通过对周期序列和叠加有周期成分的非线性序列的分析,研究序列周期性对熵测度算法的影响,并通过对心率变异性（hear trat evariability,HRV）序列在去除非平稳趋势前后的对比分析,研究序列平稳性对熵测度算法的影响。结果在序列长度范围内,不同重复频率的周期序列熵测度不同,不同比重的周期成分叠加到非线性序列中引起序列熵测度的变化也不同。生理信号时间序列中大都存在非平稳成分,而非平稳成分会降低序列的复杂度,因此进行熵测度计算前首先要去除非平稳成分。结论周期性和非平稳成分显著影响生理信号时间序列的熵测度算法。