Objective To investigate the feasibility of reduced radiation dose for CT pulmonary angiography (CTPA) and the possible lowest radiation threshold by a phantom study.Methods The CT value difference between air within the trachea and the extracorporeal background region was measured in132 consecutive patients.A noise-measurement phantom and a pulmonary embolism (PE) phantom were made of phenol-formaldehyde, and both phantoms and a water phantom were scanned with standard and lower radiation doses as follow: 280, 200, 160, 100, 90, 80, 70, 60, 50, 40, 30, 20, 15, and 10 mA respectively, at a fixed voltage of 120 kVp.Standard and soft tissue algorithms were used to reconstruct the images.Three experienced doctors independendy evaluate the image quality and the efficiency of detecting PE of the images with various doses.The Pearson correlation analysis, two-tailed paired t test, ANOVA, and Kappa test were employed for the statistical analysis.Results The CT value difference between air within the trachea and the extracorpereal background region in 132 consecutive patients ranged from 20.00 to 55.00 HU, which had a positive correlation with weight[(64.99±11.86) kg], weight-height ratio [(38.71±6.13) kg/m], and BMI[(23.11±3.38) kg/m2](r=0.228,0.374,0.449 respectively; P <0.01).The image noise level with soft-tissue reconstruction algorithm[(16.55±9.08), (16.42±9.40) HU]was significantly lower than that of the image with standard reconstruction algorithm[(22.43±11.25),(21.99±11.67) HU](F=4.316, P < 0.05).The image noise level with soft-tissue reconstruction algorithm at 100 mA was similar to that of the images with standard reconstruction algorithm at 280 mA, and the signal-w-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the image of PE phantom was 23.05 and 20.52 respectively, without any impairment in detectability of embolus.The image noise level with soft-tissue reconstruction algorithm at 60 mA was similar to that of the image with standard reconstruction algorithm at 160 mA, while the SNR and CNR was 18.01 and 15.97 respectively, also with acceptable detectability of embolus.When the tube current was reduced below 30 mA, the image quality decreased significantly, with the SNR and CNR was lower than 12.36 and 10.95 respectively, and the detectability of embolus was degraded.The consistency of the image quality grading by 3 observers was excellent(K=0.807,0.712,0.904 ,respectively; P < 0.01).Conclusions The 100 mA may potentially be the ideal low dose tube current setting, with radiation dose only equal to 36% of 280 mA (standard dose).The 30 mA may possibly be a minimum radiation dose for detecting PE.The soft-tiasue reconstruction algorithm was favorable in preserving the SNR when the radiation dose was reduced.

OBJECTIVE： To establish antibiotics use rationality evaluation model in type Ⅰ incision surgery patients， and to provide reference for prescription review of clinical pharmacists. METHODS： Totally 432 inpatients underwent type Ⅰ surgical incision in a hospital from Jan. 1st- Dec. 31st， 2017 were selected as the research objects. The information of diagnosis and treatment including age， nosocomial infection， the number of kinds of antibiotics used were extracted. Based on the results of clinical pharmacists’ comments on the antibiotics use rationality in patients’ prevention and treatment， non-conditional Logistic regression and support vector machine （SVM） in machine learning method were used to convert clinical pharmacists’ comments into objective index that can be recognized by the machine learning model， using categories of antibiotics （preventive or therapeutic use） as dependent variables and the patient’s diagnosis and treatment information as independent variables. Classification and identification model was established for antibiotics use rationality in type Ⅰ incision surgery patients. Using sensitivity， specificity and Youden index as indexes， established mode was validated on the other 61 samples of type Ⅰ incision surgery patients. The rationality of antibiotics prescriptions in type Ⅰ incision surgery patients before （by manual review， Jan.-Dec. 2017） and after （Jan.-Oct. 2018） using the model were collected， and the effects of the model were evaluated. RESULTS： The sensitivity， specificity and Youden index of non-conditional Logistic regression model were 65.63％， 75.00％ and 40.63％， respectively. Main parameters of the model established by SVM included gamma 0.01， cost 10， sensitivity 92.19％， specificity 87.50％， Youden index 79.69％. The model established by SVM was better than non-conditional Logistic regression. SVM was used to validate established mode， and sensitivity， specificity and Youden index were 100％， 88.57％ and 88.57％， respectively. Compared with before using the model， the evaluation ratio increased from 69.44％ to 100％， the rate of prophylactic use of antibiotics decreased from 23.84％ to 16.43％， the rate of rational drug type selection increased from 37.86％ to 54.39％， and treatment course shortened from 5.01 days to 3.26 days after using the model. CONCLUSIONS： Established antibiotics use rationality evaluation model in typeⅠincision surgery patients by SVM in machine learning method fully covers all the patients， promotes rational use of antibiotics in typeⅠincision surgery patients， and provides a new idea for pharmacist prescription comment.

Objective:To express fusion protein of mouse thymic stromal lymphopoietin (TSLP) and HIV-1 gp120BAL V1/V2 subdomain in 293F cell.Methods:Full length of the V1V2 sequence from BAL isolate was fused with the C-terminus of mouse thymic stromal lymphopoietin (TSLP) and sub-cloned into pCEP-Pu vector to construct the eukaryotic expression plasmid-pCTV1V2BAL.The recombinant plasmid was confirmed by enzyme digestion and sequencing , then transfected into 293 F cells using PEI as a transfection reagent .The fusion protein was purified by metal chelate affinity chromatography and characterized by SDS -PAGE and Western blot . The epitopes of V1/V2 in fusion protein were identified by ELISA .Results:The SDS-PAGE and Western blot results showed that there were highly heterogeneous glycoprotein bands at the site between 35 kD and 55 kD, which reacted with anti-mTSLP rabbit polyclonal antibody and anti-His tag mouse monoclonal antibody .The ELISA analysis showed that antibodies to V 1/V2BAL existed in the sera of HIV-1 positive patients.Conclusion:The mTSLP-V1/V2 fusion protein was successfully expressed in 293F cells, which may be useful for HIV-1 vaccine research .

BACKGROUND: The detection of pulmonary nodules is a key step to achieving the early diagnosis and therapy of lung cancer. Deep learning based Artificial intelligence (AI) presents as the state of the art in the area of nodule detection, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the performance of AI in the detection of malignant and non-calcified nodules in chest CT. METHODS: Two hundred chest computed tomography (CT) data were randomly selected from a self-built nodule database from Tianjin Medical University General Hospital. Both the pathology confirmed lung cancers and the nodules in the process of follow-up were included. All CTs were processed by AI and the results were compared with that of radiologists retrieved from the original medical reports. The ground truths were further determined by two experienced radiologists. The size and characteristics of the nodules were evaluated as well. The sensitivity and false positive rate were used to evaluate the effectiveness of AI and radiologists in detecting nodules. The McNemar test was used to determine whether there was a significant difference. RESULTS: A total of 889 non-calcified nodules were determined by experts on chest CT, including 133 lung cancers. Of them, 442 nodules were less than 5 mm. The cancer detection rates of AI and radiologists are 100%. The sensitivity of AI on nodule detection was significantly higher than that of radiologists (99.1% vs 43%, P<0.001). The false-positive rate of AI was 4.9 per CT and decreased to 1.5 when nodules less than 5 mm were excluded. CONCLUSIONS AI achieves the detection of all malignancies and improve the sensitivity of pulmonary nodules detection beyond radiologists, with a low false positive rate after excluding small nodules.
Artificial Intelligence ; Deep Learning ; Humans ; Lung Neoplasms ; diagnosis ; diagnostic imaging ; Multiple Pulmonary Nodules ; diagnosis ; diagnostic imaging ; Tomography, X-Ray Computed