Acupuncture ; education ; Acupuncture Therapy ; Humans ; Moxibustion

The relationship between the expression of vascular endothelial growth factor (VEGF) and microvascular density (MVD) marked by CD105 (CD105-MVD), and that between CD105-MVD and the clinicopathological characteristics of primary pterygium were investigated. The streptavidin-biotin complex (SABC) immunohistochemical staining in paraffin-embedded tissues was used to detect the expression of VEGF in 23 cases of primary pterygia and 7 normal conjunctival specimens. The antibody against CD105 was used to display vascular endothelial cells, and MVD was examined by counting the CD105-positive vascular endothelial cells. The correlations of VEGF and CD105-MVD, and those of CD105-MVD and clinicopathological data were analyzed by using SPSS 12.0. The expression of VEGF was significantly increased in epithelia (P=0.000), endothelia and stroma cells (P=0.005) in primary pterygia as compared with normal conjunctivae. The CD105-MVD in pterygia (mean 19.22±6.68) was higher than that in normal conjunctivae (mean 4.00±2.15, P=0.000). MVD in pterygia was significantly associated with the Tan classification (P=0.000) and the VEGF expression level in the stroma (P=0.020), but not with sex (P=0.61), age (P=0.150) or the VEGF expression level in the epithelia (P=0.518). Our results suggest that over-expression of VEGF and high CD105-MVD in primary pterygium may contribute to the progression by increasing angiogenesis and growth of primary pterygium.

Objective: To investigate a cluster of coronavirus disease 2019(COVID-19)caused by latent infection in Haikou,so as to provide reference for the prevention and control of COVID-19 clusters. Methods: An epidemiological investigation was conducted according to the COVID-19 Prevention and Control Program(Fourth Edition). The course of diagnosis and treatment,clinical characteristics and field investigation data were collected to analyze the transmission chain and the intergeneration between cases. Results : Among 39 people involved,five confirmed cases and two asymptomatic infections were found,with an attack rate of 17.95%. The cases aged from 40 to 65 years and lived in the dormitories near the farm of Dongshan. The first case(named Case 1)closely contacted with a confirmed case of COVID-19 in Chengmai from January 28 to February 10,was isolated on February 13 and developed symptoms on February 16. The other six cases(Case 2 to Case 7)shared the water source with Case 1 from January 28 to February 13(within the incubation period of Case 1). They needed to open the power switch outside Case 1's room and the water valve in Case 1's washroom before the use of water. They might be infected by contacting the doorknob and the water valve contacted by Case 1,or by the aerosol formed after Case 1 used the washroom,then infected each other when living together. The onset of Case 2 to Case 7 was earlier than Case 1,and they had no travelling history in Hubei Province 14 days before and contacted no confirmed cases except Case 1. Therefore,Case 1 was the source of the cluster during his incubation period of COVID-19. Conclusion This was a cluster of COVID-19 due to latent transmission by living in the same area and touching the same objects indirectly,which indicated that COVID-19 was infectious in the incubation period.

Objective: To analyze the relationship between the serum levels of low density lipoprotein cholesterol (LDL-C) and achilles tendon thickness (ATT).
Methods: We studied 154 patients with high serum level of LDL-C (LDL-C≥3.37 mmol/L) from 2014-03 to 2015-03, the patients were at (18-75) years of age. According to《Guidelines on Prevention and Treatment of Blood Lipid Abnormality in Chinese Adults 2007》, the patients were divided into 2 groups:Borderline high LDL-C (3.3-4.12 mmol/L) group n=50 and High LDL-C (≥4.14 mmol/L) group, n=104;in addition, there was a Normal control group (LDL-C<3.37 mmol/L), n=51. ATT values were measured by standard digital radiography and the results were compared among 3 groups;the relationship between serum levels of LDL-C and ATT was studied.
Results: ATT levels in High LDL-C group (9.42 ± 3.63) mm was higher than Borderline high group (8.24±1.73) mm and Normal control group (6.05±0.28)mm, all P<0.05. The liner correlation coefifcient of serum level of LDL-C and the mean ATT was (r=0.346, P<0.001).
Conclusion: Our preliminary research showed that the higher serum level of LDL-C associated with thicker ATT, they had positive correlation. This phenomenon should be further conifrmed by large sample and multi-center investigation.

The relationship between the expression of vascular endothelial growth factor (VEGF) and microvascular density (MVD) marked by CD105 (CD105-MVD),and that between CD105-MVD and the clinicopathological characteristics of primary pterygium were investigated.The streptavidin-biotin complex (SABC) immunohistochemical staining in paraffin-embedded tissues was used to detect the expression of VEGF in 23 cases of primary pterygia and 7 normal conjunctival specimens.The antibody against CD105 was used to display vascular endothelial cells,and MVD was examined by counting the CDl05-positive vascular endothelial cells.The correlations of VEGF and CD105-MVD,and those of CD105-MVD and clinicopathological data were analyzed by using SPSS 12.0.The expression of VEGF was significantly increased in epithelia (P=0.000),endothelia and stroma cells (P=0.005) in primary pterygia as compared with normal conjunctivae.The CD105-MVD in pterygia (mean 19.22±6.68) was higher than that in normal conjunctivae (mean 4.00±2.15,P=0.000).MVD in pterygia was significantly associated with the Tan classification (P=0.000) and the VEGF expression level in the stroma (P=0.020),but not with sex (P=0.61),age (P=0.150) or the VEGF expression level in the epithelia (P=0.518).Our results suggest that over-expression of VEGF and high CD105-MVD in primary pterygium may contribute to the progression by increasing angiogenesis and growth of primary pterygium.

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.