Objective: To explore the clinical dosimetry advantages of automated plan of IMRT for postoperative esophageal cancer and the dose prediction accuracy of the constructed 3D U-Res-Net model． Methods: A total of 110 postoperative esophageal cancer (middle and upper) cases treated by IMRT were considered in the study，of which 90 cases were randomly selected for training of deep learning prediction model．The deep learning prediction model and Auto-Plan module ( Philips pinnacle3 16. 2 ) were used to predict the three-dimension dose distribution and redesigned the remaining 20 cases respectively ，and the results obtained were compared with manual plan. Results : The average DSC value between the deep learning prediction plan and the manual plan was greater than 0. 92 in isodose surface，and the average Hausdorff distance HD95 of the isodose surface was 0. 58－0. 62 cm ; The V20 ，V30 ，Dmean of total lung were slightly lower than those of manual plan (P ＜0. 05 ) for the prediction model， meanwhile，the D2 ，D50 ，Dmean，HI of the target area and V30 of total lungs were better than those of manual plan(P ＜0. 05) for Auto-Plan ; Three-dimensional dose distribution of the three groups and the corresponding DVH curve showed that the three-dimensional dose distribution of the three groups had a little differences，and the DVH curves of the target area and organs at risk had a good agreement． Conclusion Auto-Plan can realize the design of automated plan for postoperative esophageal cancer，while the deep learning prediction model can realize the accurate prediction of the 3D dose distribution.

Objective:To propose a deep learning network model 2D-PE-GAN to automatically delineate the target area of nasopharyngeal carcinoma and improve the efficiency of target area delineation.Methods:The model adopted the architecture of generative adversarial networks which used a UNet similar structure as the generator, and 2D-PE-block was added after each layer of convolution operation of the generator to improve the accuracy of delineation. The experimental data included CT images from 130 cases of nasopharyngeal carcinoma. The images were preprocessed before model training. In addition, three models of UNet, GAN, and GAN with an attention mechanism were compared, and Dice similarity coefficient, Hausdorff distance, accuracy, Matthews correlation coefficient, Jaccard distance were employed to evaluate network performance.Results:Compared with UNet, GAN and GAN with the attention mechanism, the average Dice similarity coefficient of 2D-PE-GAN network segmentation of CTV was increased by 26%, 4% and 2%. The average Dice similarity coefficient of GTV segmentation was increased by 21%, 4%, 2%, respectively. Compared with the GAN network with the attention mechanism, the parameters and time of 2D-PE-GAN were reduced by 0.16% and 18%, respectively.Conclusions:Compared with the above three networks, 2D-PE-GAN network can increase the segmentation accuracy of nasopharyngeal carcinoma target area delineation. At the same time, compared with the attention mechanism with similar reasons, 2D-PE-GAN network can reduce the occupation of computing resources when the segmentation accuracy is not much different.

Objective:To develop a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans, and to verify the clinical feasibility and clinical value of the method .Methods:The 3D U-Netwas trained using the radiotherapy plans of 45 rectal cancer cases that were formulated by physicists with more than five years of radiotherapy experience. After obtaining 3D dose distribution using 3D U-Net prediction, this study established the plan quality metrics of intensity modulated radiotherapy(IMRT) rectal cancer radiotherapy plans using dose-volume histogram(DVH) indexes of dose prediction. Then, the initial scores of rectal cancer radiotherapy plans were determined.Taking the predicted dose as the optimization goal, the radiotherapy plans were optimized and scored again. The clinical significance of this scoring method was verified by comparing the scores and dosimetric parameters of the 15 rectal cancer cases before and after optimization.Results:The radiotherapy plans before and after optimization all met the clinical dose requirements. The total scores were(77.21±9.74) before optimization, and (88.78±4.92) after optimization. Therefore, the optimized radiotherapy planswon increased scores with a statistically significant difference( t=-4.105, P<0.05). Compared to the plans before optimization, the optimized plans show decreased Dmax of all organs at risk to different extents. Moreover, the Dmax, V107%, and HI of PTV and the Dmax of the bladder decreased in the optimized plans, with statistically significant differences ( t=2.346-5.771, P<0.05). There was no statistically significant difference in other indexes before and after optimization ( P>0.05).The quality of the optimized plans were improved to a certain extent. Conclusions:This study proposed a dose prediction-based quantitative evaluation method of the quality of radiotherapy plans. It can be used for the effective personalized elevation of the quality of radiotherapy plans, which is beneficial to effectively compare and review the quality of clinical plans determined by different physicists and provide personalized dose indicators. Moreover, it can provide great guidance for the formulation of clinical therapy plans.

Objective:To develope a self-adjustable automatic planning method of intensity modulated radiotherapy based on predicted dose, in order to enhance the robustness of automatic planning.Methods:After the patients′ dose by 3D U-Res-Net_B network was predicted, the current dose was calculated based on the last iteration result, then the predicted dose was combined to calculate the target dose and optimized. With all iterations completed or exit conditions satisfied, final treatment plannings would be acquired. A total of 30 cases of rectal cancer were tested to verify the effectiveness of the algorithm.Results:The mean value of planning target volumes′ V100% was (95.03±0.91)% for clinical plans, close to (94.67±1.96)% for automatical plans( P>0.05), and better than (92.90±2.13)% for predicted dose with the statisically significant difference ( t=29.0, P<0.05). Automatic planning′s indexes such as V35 of small intestines, V40 of bladders and V20 - V40 of femoral heads were lower than predicted and clinical ones, with the statisically significant difference( t=4.5-118.0, P<0.05). Discrepancy in other indexes of organs at risk was not statistically significantly different( P>0.05). Conclusions:This method made automatic planning processes more robust and more adaptive to difficult clinical situations.

OBJECTIVE：To study the spectrum-effect relationship of HPLC finger print of different polar parts of Ampelopsis grossedentata with its in vivo anti-inflammatory effect. METHODS ：A. grossedentata was reflux extracted with 70％ ethanol，then extracted with petroleum ether ，chloroform，ethyl acetate and water saturated n-butanol；or it was directly decocted with water and then concentrated to obtain different polar parts. The xylene-induced mice ear swelling model was established ；using dexamethasone as positive control ，anti-inflammatory activity of different polar parts of A. grossedentata was investigated. Fingerprints of different polar parts of A. grossedentata were established by HPLC. The determination was performed on Poroshell 120 EC-C18 column with mobile phase consisted of acetonitrile- 0.1％ phosphoric acid solution （gradient elution ）at the flow rate of 1 mL/min. The column temperature was 25 ℃. The detection wavelength was set at 365 nm，and sample size was 5 μL. The grey ralational analysis method was used to analyze the spectrum-effect relationship of HPLC fingerprint common peaks of different polar parts of A. grossedentata with its anti-inflammatory effect. The correlation coefficient and correlation degree were calculated and ranked. RESULTS：Anti-inflammatory experiment showed that the anti-inflammatory effects of 70％ ethanol extraction part ，ethyl acetate extraction part and water extraction part were the most significant （inhibitory rates of ear swelling were 54.07％，30.54％， 30.45％）. Five common peaks were determined in HPLC fingerprints of different polar parts from A. grossedentata . The spectrum-effect analysis results showed that the correlation of5 common peaks were higher than 0.6；among them ，peak 3 and peak 2 （dihydromyricetin） had the strongest anti- inflammatory effect ，and their correlation degrees were both mail：123745789@qq.com greater than 0.8. CONCLUSIONS ： The anti-inflammatory effect of A. grossedentata on xylene-induced ear swelling in mice is the result of multi-comp onent synergy ； unknown substance of peak 3 and dihydromyricetin may be the main active components of A. grossedentata .

Objective:To develop a deep learning model for predicting three-dimensional (3D) voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT).Methods:A total of 110 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 90 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net was constructed to predict 3D dose distributions. Three types of 3D matrices from CT images, structure sets and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of IMRT dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: the average dose prediction bias and mean absolute errors (MAEs)of all voxels within the body, the dice similarity coefficients (DSCs), Hausdorff distance(HD 95) and mean surface distance (MSD) of different isodose surfaces were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; the dosimetric index (DI) including homogeneity index, conformity index, V50, V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired-samples t test. Results:For the 20 testing cases, the average prediction bias ranged from -2.12% to 2.88%, and the MAEs varied from 2.55% to 5.75%. The DSCs value was above 0.9 for all isodose surfaces, the average MSD ranged from 0.21 cm to 0.45 cm, and the average HD 95 varied from 0.61 cm to 1.54 cm. There was no statistically significant difference for all DIs, except for bladder Dmean. Conclusions:This study developed a deep learning model based on 3D U-Res-Net by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT.

Objective:To investigate the optimal dose of dexmedetomidine combined with propofol for anesthesia in patients undergoing modified electroconvulsive therapy (MECT).Methods:One hundred and sixty patients of both sexes, aged 20-60 yr, weighing 45-80 kg, of American Society of Anesthesiologists physical status Ⅰ or Ⅱ, scheduled for elective MECT, were allocated into 4 groups ( n=40 each) by a random number table method: different doses of dexmedetomidine combined with propofol group (D 1, D 2 and D 3 groups) and routine anesthesia group (group C). Dexmedetomidine 0.2, 0.4 and 0.6 μg/kg were intravenously injected in D 1, D 2 and D 3 groups, respectively, the equal volume of normal saline was given instead in group C, and propofol 1.0 mg/kg and succinylcholine 0.5 mg/kg were intravenously injected in turn 10 min later.Venous blood samples were collected before giving dexmedetomidine (T 0) and at 1 min after the end of MECT (T 1) for determination of the plasma epinephrine (E) and norepinephrine (NE) concentrations.Propofol consumption, occurrence of cardiovascular events, duration of epilespsy and energy suppression index were recorded. Results:Compared with group C, the plasma E and NE concentrations were significantly decreased at T 4, and the propofol consumption was reduced in D 1, D 2 and D 3 groups ( P<0.05). Compared with group D 2, the plasma E and NE concentrations were significantly increased at T 1 in group D 1 and decreased at T 1 in group D 3 ( P<0.05). The incidence of adverse cardiovascular events was significantly increased in group D 3 than in the other 3 groups ( P<0.05). There was no significant difference in duration of epilespsy or energy suppfession index among the 4 groups( P>0.05). Conclusion:The optimal dose of dexmedetomidine combined with propofol 1.0 mg/kg is 0.4 μg/kg when used for anesthesia in the patients undergoing MECT.

Objective To evaluate the feasibility of utilizing dose-volume histogram (DVH) prediction models of organs at risk (OARs) to deliver automatic treatment planning of prostate cancer.Methods The training set included 30 cases randomly selected from a database of 42 cases of prostate cancer receiving treatment planning.The bladder and rectum were divided into sub-volumes (Ai) of 3 mm in layer thickness according to the spatial distance from the boundary of planning target volume (PTV).A skewed normal Gaussian function was adopted to fit the differential DVH of Ai,and a precise mathematical model was built after optimization.Using the embedded C++ subroutine of Pinnacle scripa,ahe volume of each Ai of the remaining validation set for 12 patients was obtained to predict the DVH parameters of these OARa,ahich were used as the objective functions to create personalized Pinnacle script.Finalla,automatic plans were generated using the script.The dosimetric differences among the original clinical plannina,aredicted value and the automatic treatment planning were statistically compared with paired t-test.Results DVH residual analysis demonstrated that predictive volume fraction of the bladder and rectum above 6 000 cGy were lower than those of the original clinical planning.The automatic treatment planning significantly reduced the V70,V60,V50 of the bladder and the V70 and V60 of the rectum than the original clinical planning (all P＜0.05),the coverage and conformal index (CI) of PTV remained unchangea,and the homogeneity index (HI) was slightly decreased with no statistical significance (P＞ 0.05).Conclusion The automatic treatment planning of the prostate cancer based on the DVH prediction models can reduce the irradiation dose of OARs and improve the treatment planning efficiency.

For the past several decades, the infectious disease profile in China has been shifting with rapid developments in social and economic aspects, environment, quality of food, water, housing, and public health infrastructure. Notably, 5 notifiable infectious diseases have been almost eradicated, and the incidence of 18 additional notifiable infectious diseases has been significantly reduced. Unexpectedly, the incidence of over 10 notifiable infectious diseases, including HIV, brucellosis, syphilis, and dengue fever, has been increasing. Nevertheless, frequent infectious disease outbreaks/events have been reported almost every year, and imported infectious diseases have increased since 2015. New pathogens and over 100 new genotypes or serotypes of known pathogens have been identified. Some infectious diseases seem to be exacerbated by various factors, including rapid urbanization, large numbers of migrant workers, changes in climate, ecology, and policies, such as returning farmland to forests. This review summarizes the current experiences and lessons from China in managing emerging and re-emerging infectious diseases, especially the effects of ecology, climate, and behavior, which should have merits in helping other countries to control and prevent infectious diseases.
Behavior ; China ; epidemiology ; Climate ; Communicable Diseases ; classification ; epidemiology ; Communicable Diseases, Emerging ; epidemiology ; Disease Outbreaks ; Ecology ; Forecasting ; Humans ; Incidence

Objective To investigate the safety and efficacy of endoscopic submucosal dissection ( ESD) for early stage colorectal cancer and precancerous lesions. Methods Clinical data of 108 patients who received ESD for early stage colorectal cancer and precancerous lesions from December 2016 to June 2017 in Renmin Hospital of Wuhan University were analyzed. The lesion characteristics, postoperative pathological features, intraoperative and postoperative complications and postoperative follow-up outcomes were analyzed. Results The 108 patients all underwent ESD successfully with median operation time of 45 min. The rate of intraoperative perforation and postoperative delayed bleeding was 2. 8％ ( 3/108) and 2. 8％ (3/108), respectively. No postoperative delayed perforation occurred. Postoperative pathology showed that there were 41 cases ( 38. 0％) of tubular adenoma, 4 ( 3. 7％) villous adenoma, 39 ( 36. 1％) villous tubular adenoma [ including 41 ( 38. 0％) low-grade intraepithelial neoplasia and 16 ( 14. 8％) high-grade intraepithelial neoplasia] , 19 ( 17. 6％) adenocarcinoma, and 5 ( 4. 6％) other types. Among the 19 cases of adenocarcinoma, there were 11 cases of well-differentiated, 5 median-differentiated and 3 low-differentiated. The complete resection rate was 100. 0％ and the en bloc resection rate was 92. 3％ ( 100/108) . The mean follow-up time was 8. 1 months, and no recurrence was found during this period. Conclusion ESD is safe and effective in the treatment of early stage colorectal lesions. It is important to improve preoperative assessment, strengthen surgical skills, analyze postoperative pathological features and regularly follow up to guarantee the treatment quality of ESD.