How to suppress noise is necessary to consider the question of focus in hardware design.The external noise can be avoided or reduced by the way of reasonable design.The variety of influencing factors on power supply noise of the operational amplifier circuits are analyzed in more details and put forward corresponding solutions,in order to more fully exert low-noise op amp performance.

Since the body is a good conductor,the pacing signals and cardio-signals can be acquired together by ECG measuring circuit. The high pacing signals interfere with the processing of cardio-signal. This paper introduces a new method for restraining pacing signals and compares it with the traditional one based on slew inhibition. The result indicates that the new method is superior to the traditional one.

With the constant change of medical electronic instruments ( MEI ) professionals' needs,the professional personnel training programs need to be adjusted and improved.Based on the analysis of the MEI professional students'employment situation,this article discusses several key issues of the personnel training program,and puts forward some suggestions of improving the MEI professional's personnel training program,in combination with the actual running in the training,the training objective,course arrangement,practice teaching etc..

Objective To explore a novel long-circulating dual-receptor targeting and dual-modal molecular probe and investigate its physicochemical properties and targeting effect on breast cancer cells in vitro. Methods Dual-receptor targeting and dual-modal molecular probe RGD@BBN-lipo(QDs)-SPIO was synthesized in the following steps: long-circulating liposome was prepared by film dispersion method;water-soluble superparamagnetic iron oxide (SPIO) nanoparticles and Quantum dots (QDs) were loaded in the hydrophilic and hydrophobic layer of liposome, respectively;RGD and BBN polypeptides were coupled on the former functional magnetic/fluorescent liposomes. Stability of the probe in different physiological solutions was investigated. Transmission electron microscopy (TEM) and particle size analyzer were used to measure nanoparticle sizes and the Zeta potential. Characterization of RGD and BBN was investigated through 1H-NMR and elemental analysis. The MRI T2 relaxivities (1/T2) of RGD@BBN-lipo(QDs)-SPIO was measured through T2 map scanning on 3.0 T MR system. HUV-EC-C cells were used for assessment of cells viability by MTS assay. Prussian blue staining and fluorescence imaging were carried out to determine the targeted breast cellular uptake of RGD@BBN-lipo(QDs)-SPIO nanoparticles. Results The targeting magnetic/fluorescent dual-model molecular probes appeared spherical or para-spherical,with a mean diameter of(118.2±3.9)nm,Zeta potential of (-24.78±1.68) mV,MR T2 magnetic relaxation rate of 0.498 1× 106 M-1 · s-1.RGD and BBN polypeptides were successfully coupled on the former functionally magnetic/fluorescent liposomes with the bind rates of 33.05%and 45.06%, respectively. There was low cytotoxity of the molecular probe on human umbilical vein endothelical cells(HUV-EC-C)by MTS study. Prussian blue staining and fluorescence imaging studies showed that the RGD@BBN-lipo(QDs)-SPIO nanoparticles could target any αvβ3 or gastrin releasing peptide receptor overexpression breast cancer. Conclusions RGD@BBN-lipo(QDs)-SPIO is a novel long-circulating dual-receptor targeting and dual-modal molecular probe and has excellent physicochemical properties and stability, high T2 relaxivities and strong targeting effect on cancer cells and has laid a solid foundation for early diagnosis of breast cancer.

Objective To explore the CT dominant phase and optimal classification model in differenting clear cell renal cell carcinoma (ccRCC) from fat‐poor angiomyolipoma (fpAML) through quantitative multiple‐phase CT radiomic features analysis. Methods Clinical and imaging data of 195 cases pathologically confirmed ccRCC (n=131) and fpAML (n=64) were retrospectively studied. All the patients underwent non‐contrast enhanced CT scans and dynamic multi‐phase (corticomedullary phase, medullary phase and excretion phase) contrast‐enhanced CT scans. Regions of interest (ROIs) were manually delineated based on the selected image slices with the maximal diameter of the lesion using ITK‐SNAP software, followed by the acquisition of candidate CT radiomic feature sets from each phase with statistically significant differences by using Mann‐Whitney U test. Then, using the synthetic minority oversampling technique (SMOTE), 232 classification models which are composed of 29 different feature selection algorithms (top 10 features were chosen by the backward elimination method) and 8 different classifiers were constructed. Employing the 5‐fold cross‐validation method, the performance of each classification models for each phase was evaluated using accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under receiver operating characteristic curve (AUC), to acquire dominant CT phases and the optimal classification models for distingushing ccRCC and fpAML, along with the key imaging radiomic features. Results In this study, the mean maximal diameter of ccRCC and fpAML lesions were (3.9±1.4) cm, and (3.5±1.7) cm, respectively, and there was no statistically significant difference in the size of the tumor between two groups (P>0.05). From 102 initial imaging feature sets, the total number of candidate imaging feature sets (P<0.05) were:non‐enhanced phase (n=26), corticomedullary phase (n=71), medullary phase (n=68), excretion phase (n=62). Among the 232 classification models through different combination of classifiers and feature selectors, the amount of classification models which achieved the maximum of AUC value (AUCmax) from different CT phases were: non‐enhanced phase (n=106, 45.7%), corticomedullary phase (n=94, 40.5%), medullary phase (n=23, 9.9%), excretion phase (n=9, 3.9%). Imaging features from non‐enhanced phase and corticomedullary phase yielded higher performance compared with medullary phase and excretion phase, with the corresponding optimal prediction models were SVM‐fisher_score (AUC: 0.897, ACC: 83%, SEN: 84%, SPE:80%) and Logistic Regression‐RFS (AUC: 0.891, ACC: 83%, SEN: 81%, SPE: 89%), respectively. Conclusions The quantitative imaging features from non‐enhanced and corticomedullary phase have better performance among proposed classification models than that from medullary phase and excretion phase. Furthermore, it is feasible to acquire proper combination of feature selection and classifiers to achieve high performance in identifying ccRCC and fpAML.