[Objects]To isolate and identify the pathogen of the large outbreak of dengue in Guangdong province in 2014. To understand the origin and the phylogenetic characteristics of the isolates ,and provide scientific foundation for the surveillance and prevention of dengue fever.[Methods]Collected the patient serum samples over all the Guangdong province during the 2014 outbreakperiod,isolated and identified the virus from these samples. Amplified complete E gene and complete genome with certain primers and sequenced all the products. Then the Phylogenetic ,Bayesian phylogeography and mutations analysis were carried.[Results]40 DENV-1 strains were isolated and identified. 40 complete E gene sequences and 6 complete genome sequences of DENV-1 were obtained. Phylogenetic analysis with E gene sequences revealed that the 40 isolates were classified into two genotypes including 16 genotypeⅠ(Asia)and 24 genotypeⅤ(America/Africa). 14 genotypeⅠisolates were clustered closest with isolates from Guangdong province(2013)and Sigapore(2013)which share the nucletide identities of 99.6% ~ 99.9%,other two genotypeⅠisolates were clustered with strains from Malaysia (2013) and both share the nucletide identities of 99.7%;24 genotypeⅤisolates were all classified in one clade with striains from Bangladesh(2009),China(2009)and Bhutan(2013)which share nucletide identities of 99.0%-99.9%. Further analysis with six complete genome sequences showed that five isolates were clustered closest with strains isolated from Guangdong province(2013)share the nucletide identities of 99.6%-99.8% while the sixth stains closest with strains isolated from Myanmar(2002)share the nucletide identities of 98.8%. The isolates have five amino acid mutations compared with strains epidemic in Guangdong province in 2013,three mutations(S88V,E203G,T275R)are in the EⅡdomain and one mutation (S305P)is in the EⅢdomain which associated with virulence.[Conclusions]During the outbreak in Guangdong province in 2014, DENV-1 is the predominant causative serotype,and there are at least two different kinds of genotypes of DENV-1 largely epidemiced in the whole province. Evolution analysis reveals the multiple origins of the isolates which may origin from Guangdong province , Sigapore,Malaysia,Myanmar so that we should enhance the study and surveillance of autochthonous and vectors in order to understand the epidemic way of dengue in Guangdong province. The isolates have had four mutations in the domain associated with virulence which remain further study to know their biological effects.

Objective The aim was to evaluate the diagnostic value of MRI in lesions with architectural distortion manifested in mammography. Methods A retrospective analysis of MRI was performed in 60 patients with 61 lesions manifested as architectural distortion in full?field digital mammography (FFDM) and subsequently confirmed by pathology or two year's follow?up, 30 were malignant and 31 were benign. All the patients underwent MRI within 2 weeks of mammography. MRI protocol included conventional MR, DWI and dynamic contrast?enhanced MRI. The breast imaging reporting and data system (BI?RADS) was used as the reference standard. BI?RADS categories 1 to 3 were considered as negative for malignancy and BI?RADS categories 4 to 5 were considered as positive for malignancy. ADCs of suspicious lesion of interest and glandular tissue were calculated. nADC was then calculated using the following formula:nADC=ADC(lesion)/ADC(glandular tissue). ADC and nADC were compared by using t test. ROC analysis was carried out to define the most effective threshold ADC and nADC value to differentiate malignant from benign lesion in the breast. Diagnostic performance of the FFDM, DCE?MRI and DCE?MRI combined nADC were calculated. Results ADC value of malignant and benign lesions was (1.35±0.31)×10?3 mm2/s and (1.07±0.40)×10?3 mm2/s, respectively . nADC values were 0.83±0.17 and 0.59± 0.25, respectively (t values were 2.82 and 4.54, P<0.01). Area under the curve of ADC and nADC were 0.829 and 0.753 respectively. When threshold of ADC was set at 1.19×10?3mm2/s, sensitivity and specificity were 71.0%and 86.7%, respectively. For a nADC value threshold of 0.589, sensitivity and specificity were 93.5%and 76.7%, respectively. Sensitivity, specificity and accuracy with FFDM were 80.0%(24/30), 9.7%(3/31) and 44.3%(27/61), Sensitivity, specificity and accuracy with DCE?MRI were 90.0%(27/30), 41.9%(13/31) and 65.6%(40/61), Sensitivity, specificity and accuracy with DCE?MRI combined nADC were 93.3%(28/30), 77.4%(24/31) and 85.2%(52/61), respectively. Conclusion Sensitivity and specificity with DCE?MRI combined nADC is higher, and DCE?MRI combined nADC values is helpful to differentiate malignant from benign lesions with architectural distortion manifested in FFDM.

Objective: To investigate the value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) parameters measured using different methods in differentiating benign and malignant breast mass lesions. Methods Totally 59 patients (62 mass lesions) with breast mass lesions verified by pathologic results or follow up were enrolled. All patients underwent MR scanning, including dynamic contrast-enhanced MRI, IVIM DWI and DKI. ROI were placed at the most enhanced location, and the parameters of standard ADC (ADCstand), slow ADC (ADCslow), mean kurtosis (MK) and mean diffusion (MD) were measured. The whole lesions on the maximum slice were drawn as ROI, and the ADCstand-max, ADCslow-max, ADCfast-max, MK-max, MD-max were obtained. The differences of these parameters between benign and malignant breast lesions were analyzed. The diagnostic performance of these parameters was evaluated by ROC curve, the AUC was compared between the two methods. Results: There were 26 malignant lesions and 36 benign lesions. All the parameters (ADCstand, ADCstand-max, ADCslow, ADCslow-max, MK, MK-max, MD, MD-max) were significantly different between malignant and benign lesions (all P<0.001). AUC of ADCslow combined MK was the highest (0.915), with the sensitivity of 88.9% and specificity of 84.6%. The differences of AUC between ADCstand and ADCstand-max (Z=1.465, P=0.143), ADCslow and ADCslow-max (Z=1.013, P=0.311), MK and MK-max (Z=1.021, P=0.307), MD and MD-max (Z=1.428, P=0.153) were not statistically significant. Conclusion: For breast mass lesions, all DKI and IVIM DWI parameters are helpful to differentiating malignancy from benign lesions, and these parameters measured with different methods show equal diagnostic efficiency.

Objective To evaluate the value of IVIM-DWI and DTI parameters in quantitative analysis and differential diagnosis of invasive breast carcinoma of no special type(NST).Methods We retrospectively analyzed 60 patients (63 lesions)who underwent MR examination in our hospital and all lesions were verified by pathologic results.MR protocol included DCE-MRI,IVIM-DWI using 14b values and DTI.The ADC,ADCslow,ADCfast,f,λ1of lesions were measured and compared by two independent samples t test between the benign lesions and NST.Logistic regression analysis was made using ADC,ADCslow,f,λ1as predictors in detecting and differentiating the NST,ROC analysis was performed to compare diagnostic performance based on the area under the curve(AUC).Results The ADC,ADCslow,ADCfast,f andλ1of NST were (1.49±0.63)×10-3mm2/s,(1.32±0.49)×10-3mm2/s,(25.98±21.84)×10-3mm2/s,0.20±0.13 and (4.98±0.47)×10-3mm2/s,these values of benign lesions were (2.31±0.66)×10-3mm2/s,(2.24±0.65)×10-3mm2/s,(18.71± 12.26)×10-3mm2/s,0.33±0.15 and(5.59±0.59)×10-3mm2/s.All parameters except ADCfast(P=0.271)had significantly statistical differences (P<0.000 1)between NST and benign lesions.The regression model showed that ADCslowwas an independent predictor in NST’s detection.Conclusion The ADC,ADCslow,f andλ1is helpful for differentiation between NST and benign lesions.The regression model is most valuable in NST detection and ADCslowis the preferred index.

Objective To improve accuracy of clinical diagnosis through analyzing the CT characteristics and clinical manifestations of patients with benign lung diseases whose CT manifestations initially led to a suspicion of lung cancer. Methods This study collected 2 239 patients of benign lung disease verified by postoperative pathology in the Department of Thoracic Surgery, Beijing Chao-yang Hospital from June 2006 to December 2016. Lesions of 173 patients (101 males and 72 females with a mean age of 56.0 years) were considered very likely to be malignant on preoperative contrast CT scan, which were sorted to 20 types of lung diseases, and the 20 types of diseases contained 907 patients diagnosed or misdiagnosed. Statistical analyses were performed using the CT and clinical characteristics of the 173 patients. Results Among the 907 patients with benign lung disease, the benign pathologies that were most commonly misdiagnosed by preoperative enhanced CT were pulmonary leiomyoma (100.0%), pulmonary actinomycosis (75.0%), pulmonary cryptococcosis (71.4%), sclerosing hemangioma (50.0%) and organizing pneumonia (44.2%). Among the 173 patients with benign diseases, the most common diseases were tuberculosis (29.5%), organizing pneumonia (28.9%), pulmonary hamartoma (6.4%) and pulmonary abscess (6.4%). In the 173 patients, 17.3% had fever, 56.6% coughing, 8.7% yellow sputum, 28.9% hemoptysis, 16.2% chest pain, 18.5% elevated leukocyte counts and 4.6% elevated carcinoembryonic antigen levels. Most of the CT manifestations consisted of nodular or mass shadows, 70.5% of which had foci≤3 cm and manifestations were similar to those of lung cancer, such as a spiculated margin (49.1%), lobulation (33.5%), pleural indentation (27.2%) and significant enhancement (39.3%). Furthermore, some patients had uncommon tumor signs, such as calcification (12.7%), central liquefactive necrosis (18.5%), satellite foci (9.8%) and multiple pulmonary nodules (42.2%). Moreover, 24.3% of the patients had enlarged lymph nodes of the mediastinum or hilum. Conclusion As the CT manifestations of some benign lung conditions are similar to those of lung cancer, careful differential diagnosis is necessary to identify the basic characteristics of the disease when the imaging results are ambiguous, and the diagnosis of a lung disease need incorporate the patients' clinical characteristics and a comprehensive analysis.

ObjectiveTo establish the specific chromatogram and thin layer chromatography（TLC） of Qingxin Lianziyin（QXLZY） benchmark samples， in order to clarify the key quality attributes and provide a reference for the quality evaluation of QXLZY. MethodHigh performance liquid chromatography（HPLC） specific chromatogram of QXLZY benchmark samples was developed by using a YMC Hydrosphere C₁₈ column（4.6 mm×250 mm， 5 μm） with the mobile phase of acetonitrile（A）-0.2% formic acid aqueous solution（B） for gradient elution（0-10 min， 5%-20%A； 10-20 min， 20%A； 20-25 min， 20%-24%A； 25-40 min， 24%-30%A； 40-55 min， 30%-50%A； 55-65 min， 50%-100%A； 65-75 min， 100%A； 75-75.1 min， 100%-5%A； 75.1-90 min， 5%A）， and the detection wavelength was 360 nm. Ultra-high performance liquid chromatography-linear ion trap/orbitrap mass spectrometry（UHPLC-LTQ-Orbitrap MS） with electrospray ionization（ESI） was used to identify the components of QXLZY benchmark samples by accurate relative molecular weight and multilevel MS fragment ion information， the detection conditions were positive and negative ion modes and data dependency scanning mode. TLC identification methods for Ophiopogonis Radix， Lycii Cortex， Nelumbinis Semen， Poria， Astragali Radix and Ginseng Radix et Rhizoma in QXLZY were established. ResultA total of 15 characteristic peaks were identified from Glycyrrhizae Radix et Rhizoma， Plantaginis Semen and Scutellariae Radix， and the relative standard deviations of the retention times of 15 characteristic peaks in 15 batches of QXLZY benchmark samples were≤3% with peak 8（baicalin） as the reference peak. A total of 100 compounds， including flavonoids， organic acids， saponins， amino acids and others， were identified in the benchmark samples by UHPLC-LTQ-Orbitrap MS. The established TLC had good separation and was suitable for the identification of Ophiopogonis Radix， Lycii Cortex， Nelumbinis Semen， Poria， Astragali Radix and Ginseng Radix et Rhizoma in QXLZY. ConclusionThe material basis of QXLZY benchmark samples is basically determined by MS designation and source attribution. The established specific chromatogram and TLC of QXLZY are simple， stable and reproducible， which can provide a reference for the development and quality control of QXLZY.