Objective To look for more serum biomarkers supporting the diagnosis of multiple system atrophy ( MSA) and providing more evidence for early treatment.Methods All patients and healthy controls were enrolled from January 2011 to March 2015 in the First Affiliated Hospital of Zhengzhou University.Demographic features and biochemical examination results were collected.The t test was used to compare the lipid levels between MSA patients and controls.LSD-t test was used to compare the lipid levels among subtypes of MSA patients.Multivariate Logistic regression analysis was conducted to analyze the influencing factors.The relevance between lipid levels and onset age, disease duration and Hoehn & Yahr stage was calculated by Spearman correlation coefficients.Results Participants included 195 MSA patients and 195 age-and gender-matched controls with no neurological diseases.The levels of total cholesterol ((4.33 ±0.90) mmol/L), triglyceride ((1.27 ±0.71) mmol/L), low-density lipoprotein (LDL;(2.70 ±0.76) mmol/L) were significantly lower in patients than in controls ((4.52 ±0.85), (1.47 ± 0.86), (2.85 ±0.71) mmol/L ,t=2.056,2.528 and 2.149 respectively, all P<0.05).The levels of total cholesterol ((4.28 ±0.96) mmol/L) and triglyceride ((1.20 ±0.64) mmol/L) were significantly lower in MSA-P patients than in control group ((4.52 ±0.85), (1.47 ±0.86) mmol/L;LSD-t=1.983, 2.566, both P<0.05).After adjusting for age, gender and histories, the odds ratio ( OR) was 0.31 (95%CI 0.15-0.65, P =0.002 ) for MSA patients in the highest quartile of triglyceride and 0.38 (95%CI 0.17 -0.83,P=0.016) for those in the highest quartile of high-density lipoprotein (HDL), compared with the lowest quartiles.And HDL level was in a significantly positive correlation with onset age (r=0.15, P=0.039).Conclusion Our data suggest that triglyceride and HDL may be associated with the prevalence of MSA, and the lower levels of HDL, the earlier onset of MSA.

There are several kinds of PICC tip positioning method in clinic at present,including body surface measurement, X-ray tip location, ultrasound guided assisted positioning and venous ECG. The limitations of former three methods were that the physical structure of the individual differences may affect the results of surface measurement accuracy; the differences of visual and subjective judgment may exist between different observers and the patient may easy to be affected by X-ray radiation; B ultrasound guidance assisted positioning can not show the whole process, and some of the central vein in the discount or too deep in the catheter tip can not be judged. The localization of the P wave in the center of the tube was observed by the technique of the intra cavity ECG localization to judge the position of the catheter tip, which can be positioned in a timely manner in order to reduce the risks caused by incorrect position of the catheter tip, avoid postoperative X-ray radiation and readjust the tube, reduce the cost of inspection. This study summarizes the PICC tip localization method, the principle, advantage, stability, influence factor and the method of improving the stability of the electrocardiogram.

Objective:To explore the application value of artificial intelligence-assisted diagnosis model based on convolutional neural network (CNN) in the differential diagnosis of benign and malignant breast masses.Methods:A total of 10 490 images of 2 098 patients with breast lumps (including 1 132 cases of benign tumor, 779 cases of malignant tumor, 32 cases of inflammation, 155 cases of adenosis) were collected from January 2016 to January 2018 in Beijing Tiantan Hospital Affiliated to the Capital University of Medical Sciences. They were divided into training set and test set and the auxiliary artificial intelligence diagnosis model was used for training and testing. Two sets of data training models were compared by two-dimensional imaging (2D) and two-dimensional and color Doppler flow imaging (2D-CDFI). The ROC curves of benign breast tumors, malignant tumors, inflammation and adenopathy were analyzed, and the area under the ROC curve (AUC) were calculated.Results:The accuracies of 2D-CDFI ultrasonic model for training group and testing group were significantly improved. ①For benign tumors, the result from training set with 2D image was: sensitivity 92%, specificity 95%, AUC 0.93; the result from training set with 2D-CDFI images was: sensitivity 93%, specificity 95%, AUC 0.93; the result for test set with 2D images was: sensitivity 91%, specificity 96%, AUC 0.94; the result for test set with 2D-CDFI images was: sensitivity 93%, specificity: 94%, AUC 0.94. ② For malignancies, the result for training set with 2D images was: sensitivity 93%, specificity 97%, AUC 0.94; the result for training set with 2D-CDFI images was: sensitivity 93%, specificity 96%, AUC 0.94; the result for test set with 2D images was: sensitivity 93%, specificity 96%, AUC 0.94; the result for test set with 2D-CDFI images was: sensitivity 93%, specificity 96%, AUC 0.94. ③For inflammation, the result for training set with 2D images was: sensitivity 81%, specificity 99%, AUC 0.91; the result for training set with 2D-CDFI images was: sensitivity 86%, specificity 99%, AUC 0.89; the result for test set with 2D images was: sensitivity 100%, specificity 98%, AUC 0.98; the result for test set with 2D-CDFI images was: sensitivity 100%, specificity 99%, AUC 0.96. ④For adenopathy, the result for training set with 2D images was: sensitivity 88%, specificity 97%, AUC 0.94; the result for training set with 2D-CDFI images was: sensitivity 93%, specificity 98%, AUC 0.94; the result for test set with 2D images was: sensitivity 94%, specificity 98%, AUC 0.93; the result for test set with 2D-CDFI images was: sensitivity 88%, specificity 99%, AUC 0.90. Its diastolic accuracy was not affected even if the maximum diameter of the tumor was less than 1 cm.Conclusions:Through the deep learning of artificial intelligence based on CNN for breast masses, it can be more finely classified and the diagnosis rate can be improved. It has potential guiding value for the treatment of breast cancer patients.

Objective:To investigate the value of contrast-enhanced ultrasound (CEUS) in the diagnosis of malignant endometrial lesions.Methods:A total of 142 patients with endometrial lesions who underwent contrast-enhanced ultrasound examination in Beijing Tiantan Hospital, Capital Medical University from January 2019 to September 2021 were selected. The endometrial lesions were divided into benign group (including endometrial hyperplasia and endometrial polyps) and malignant group (endometrial cancer) according to the pathological results of surgery, and the differences of contrast enhancement patterns between benign group and malignant group were compared. The sensitivity, specificity of CEUS in the diagnosis of endometrial cancer were calculated, and the Kappa value was calculated with the initial enhancement time earlier than or equal to the muscularity or the peak time earlier than the muscularity as the diagnostic index for the diagnosis of endometrial malignant lesions. The sensitivity, specificity, and Kappa value of CEUS in the diagnosis of endometrial cancer and endometrial cancer with thickness ≥10 mm were calculated.Results:A total of 108 patients underwent surgery with clear pathological results, including 66 patients in the benign lesion group and 42 patients in the malignant lesion group. The thickness of malignant lesions was significantly larger than that of benign lesions, and the difference was statistically significant( t=4.039, P<0.05), but there was no significant difference of hemodynamic parameters between the two groups ( P>0.05). The initial enhancement time, peak time and peak intensity of benign lesions were significantly different from those of malignant lesions(all P<0.05). The sensitivity, specificity, and Kappa value of CEUS in the diagnosis of endometrial cancer were 64.3%, 100% and 0.668, respectively. The sensitivity, specificity and Kappa value of CEUS in the diagnosis of endometrial carcinoma with lesion thickness ≥10 mm were 75.0%, 100% and 0.795, respectively. Conclusions:For the diagnosis of endometrial lesions, especially the malignant endometrial lesions with thickness and diameter greater than or equal to 10mm, there is a high diagnostic coincidence rate between CEUS and pathological diagnosis, and endometrial malignant lesions have more specific CEUS manifestations.

Objective To observe the value of ultrasound vector flow imaging(VFI)combined with singular value decomposition(SVD)filtering for depicting deep microvasculature flow velocity of liver.Methods Grayscale ultrasound,CDFI and contrast-enhanced ultrasound(CEUS)were prospectively performed in a patient with suspected liver hemangioma.Images of CEUS were dealt with SVD filtering.Cross-correlation algorithm was used to obtain images of VFI based on grayscale ultrasound,original CEUS and SVD filtered CEUS,respectively,and the ability of the above images for depicting liver microvascular flow direction and velocity were compared.Results The signal-to-noise ratio(SNR)of liver grayscale ultrasound,original CEUS and SVD filtered CEUS images was 7.56,17.65 and 22.43 dB,respectively,while their contrast-to-issue ratio(CTR)was 1.12,7.56 and 16.34 dB,respectively.Compared with VFI based on grayscale ultrasound and original CEUS,VFI based on SVD filtered CEUS could display faster velocity and more uniform direction of blood flow.Before and after SVD filtering,liver microvascular flow velocity measured with VFI was 1.91(0.81,4.11)and 6.83(4.25,9.41)mm/s,respectively,which were significantly different(Z=-10.671,P＜0.001).Conclusion Combined with SVD filtering could significantly improve the efficiency of VFI for depicting liver deep microvasculature flow velocity.

Objective To observe the feasibility of Fourier ring correlation(FRC)for measuring the overall resolution of ultrasonic microvascular images.Methods Liver contrast-enhanced ultrasound(CEUS)images of 1 patient with suspected hepatic hemangioma(dataset 1)were processed with singular value decomposition(SVD)filter(dataset 2)and Frangi filter(dataset 3),respectively.Three ROI were selected on CEUS image in each dataset,and the signal-intensity curves were drawn,and local resolution of CEUS image in each dataset was measured using full width at half maxima(FWHM)method.Then the above datasets were divided into odd frame subset and even frame subset,respectively,which were converted into frequency domain using fast Fourier transform.FRC curves were generated to calculate the overall resolution of each kind images.Results The signal-to-noise ratio(SNR)of CEUS images in dataset 1,2,3 was(19.94±2.33),(30.36±0.78)and(69.52±16.38)dB,respectively,the local resolution was(4.07±9.82),(1.53±0.04)and(1.27±0.06)mm,both successively increased(all P＜0.05).The overall resolution of CEUS images in dataset 1,2,3 was 2.07,0.91 and 0.51 mm,respectively.Conclusion FRC was feasible for measuring the overall resolution of ultrasonic microvascular images.

Objective To observe the effect of extracting different speed contrast-enhanced ultrasound(CEUS)signals using singular value decomposition filtering and implementing fused imaging for improving visualization of microvascular structures.Methods Singular value decomposition and filtering were performed on 200 frames of mouse subcutaneous tumor and 250 frames of human liver CEUS image datasets.The singular value inflection point was used as the threshold for separating low-speed and high-speed contrast signals by exponential projection of singular values on the singular value sequence.The low-speed and high-speed signals in dataset were extracted,and dual-modal fusion imaging was performed on the processed images.The image resolution differences of ultrasound microvascular imaging regions with different flow velocities were evaluated.Results The high-speed blood flow images processed by the high singular value sequence range retained the fast-moving contrast signals which mainly showed the thicker blood vessel structures,while the low-speed blood flow images processed by the low singular value sequence range retained the low-speed moving contrast signals which reflected the microvascular structures on high singular value images.The fusion imaging displayed microvascular network more completely.Conclusion Appropriate selection of singular value range was crucial to optimization of CEUS images using singular value decomposition filtering.Fusion imaging was beneficial to improving visualization of microvascular structures.

Objective: To analyze the ultrasound examination and computed tomography angiography (CTA) features of carotid web(CAW), and compare with the pathology after carotid endarterectomy, and then compare diagnostic efficacies of the two methods. Methods: From June 2018 to July 2019, 159 patients underwent carotid endarterectomy(CEA) in Beijing Tian Tan Hospital were collected, ultrasound examination and CTA were performed preoperatively. The presence or absence of CAW and whether there were thrombosis or atherosclerotic plaques associated with it were identified. The location length, thickness, direction in the lumen, echo characteristics of CAW, and complicated with or without thrombosis or atherosclerotic plaques were recorded. The postoperative specimens were observed, and the pathological analysis was performed. Results: Among the 159 cases of CEA, 22 cases were confirmed to have CAW structure by pathology, and HE staining showed extensive intimal fibrohyperplasia and mucoid degeneration, among which 18 cases had plaque formation at the bottom of the carotid web, and 4 cases associated with thrombosis. There were 17 cases of CAW structure diagnosed by ultrasound, 5 cases were misdiagnosed or missed, the sensitivity and specificity of ultrasound in the diagnosis of CAW were 77% (17/22) and 98% (135/137), and the accuracy was 75%. Eleven cases of CAW were diagnosed by preoperative CTA, and 11 cases were misdiagnosed and missed diagnosis, the sensitivity and specificity of CTA in the diagnosis of CAW were 50%(11/22) and 97%(134/137), and the accuracy was 47%. Conclusions The sensitivity of ultrasound in the diagnosis of CAW is higher than that of CTA, which can better display the structure of CAW and whether it is associated with plaque or thrombosis.

ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
Algorithms ; Arrhythmias, Cardiac ; Databases, Factual ; Electrocardiography/methods* ; Humans ; Support Vector Machine