Objective To propose a novel method based on three-dimensional depthwise separable convolution network(3D DSN)for the separation of PET images with dual tracers of 18F-FDG and 18F-FAPI.Methods A total of 120 pairs of 18F-FDG and 18F-FAPI PET images of the same patient scanned separately at different time points were collected,and the dual-tracer PET image was generated through simulation.After the image registration of PET images of two tracers for ensuring spatial position matching,the registered PET images were forward-projected to generate sinogram data,and the sinogram data of two tracers were accumulated to obtain mixed sinogram data.Subsequently,the dual-tracer PET image was reconstructed using maximum likelihood expectation maximization and input into a 3D DSN based network for image separation,thereby obtaining PET images of two single tracers.Results Compared with 3D CNN method,the proposed method increased the structure similarity index measure(SSIM)of the separated 18F-FDG images to the real 18F-FDG images by 0.87%,increased the peak signal-to-noise ratio(PSNR)by 11.8%,and reduced the normalized root mean square error(NRMSE)by 52%.The SSIM of the separated 18F-FAPI images to the real 18F-FAPI images increased by 1.1%,PSNR increased by 17.0%,and NRMSE decreased by 51%.Conclusion The proposed method can be effectively applied to simultaneous PET imaging with dual PET tracers,reducing the number of scans and costs in time and money,and providing clinical doctors more accurate and abundant diagnostic information.

Objective To propose a novel method based on three-dimensional depthwise separable convolution network(3D DSN)for the separation of PET images with dual tracers of 18F-FDG and 18F-FAPI.Methods A total of 120 pairs of 18F-FDG and 18F-FAPI PET images of the same patient scanned separately at different time points were collected,and the dual-tracer PET image was generated through simulation.After the image registration of PET images of two tracers for ensuring spatial position matching,the registered PET images were forward-projected to generate sinogram data,and the sinogram data of two tracers were accumulated to obtain mixed sinogram data.Subsequently,the dual-tracer PET image was reconstructed using maximum likelihood expectation maximization and input into a 3D DSN based network for image separation,thereby obtaining PET images of two single tracers.Results Compared with 3D CNN method,the proposed method increased the structure similarity index measure(SSIM)of the separated 18F-FDG images to the real 18F-FDG images by 0.87%,increased the peak signal-to-noise ratio(PSNR)by 11.8%,and reduced the normalized root mean square error(NRMSE)by 52%.The SSIM of the separated 18F-FAPI images to the real 18F-FAPI images increased by 1.1%,PSNR increased by 17.0%,and NRMSE decreased by 51%.Conclusion The proposed method can be effectively applied to simultaneous PET imaging with dual PET tracers,reducing the number of scans and costs in time and money,and providing clinical doctors more accurate and abundant diagnostic information.

Objective To explore the value of radiomics model and deep learning model based on unenhanced MRI in predicting microvascular invasion(MVI)of hepatocellular carcinoma(HCC)preoperatively.Methods A total of 189 patients with postopera-tive pathologically confirmed HCC from two centers were retrospectively selected,of which 119 cases from Zhujiang Hospital of Southern Medical University were used as the training set[60 cases with negative MVI,59 cases with positive MVI],and 70 cases from Shenzhen People's Hospital were used as the external test set[38 cases with negative MVI and 32 cases with positive MVI].Clinical indicators were analyzed by univariate and multivariate logistic regression analysis and the independent predictors of positive MVI were screened.Deep transfer learning(DTL)and traditional radiomics methods were used to construct radiomics model and deep learning model based on unenhanced MRI.The predictive performances of each model were compared using receiver operating charac-teristic(ROC)curves and area under the curve(AUC).DeLong test was employed to compare statistical differences in performance of the models.Results Alkaline phosphatase(ALP)and prothrombin time(PT)were independent predictors of positive MVI(P<0.05).The deep learning model based on T2WI had the best predictive efficacy,with AUC of 0.779[95%confidence interval(CI)0.696-0.863]and 0.741(95%CI 0.620-0.861)in the training set and external test set,respectively,and there were statistically significant differences compared with the radiomics model and the clinical model based on T1WI(P<0.05).Conclusion Deep learning model based on T2WI has a certain application value in preoperative noninvasive prediction of MVI status in HCC patients.

Objective To explore the value of radiomics model and deep learning model based on unenhanced MRI in predicting microvascular invasion(MVI)of hepatocellular carcinoma(HCC)preoperatively.Methods A total of 189 patients with postopera-tive pathologically confirmed HCC from two centers were retrospectively selected,of which 119 cases from Zhujiang Hospital of Southern Medical University were used as the training set[60 cases with negative MVI,59 cases with positive MVI],and 70 cases from Shenzhen People's Hospital were used as the external test set[38 cases with negative MVI and 32 cases with positive MVI].Clinical indicators were analyzed by univariate and multivariate logistic regression analysis and the independent predictors of positive MVI were screened.Deep transfer learning(DTL)and traditional radiomics methods were used to construct radiomics model and deep learning model based on unenhanced MRI.The predictive performances of each model were compared using receiver operating charac-teristic(ROC)curves and area under the curve(AUC).DeLong test was employed to compare statistical differences in performance of the models.Results Alkaline phosphatase(ALP)and prothrombin time(PT)were independent predictors of positive MVI(P<0.05).The deep learning model based on T2WI had the best predictive efficacy,with AUC of 0.779[95%confidence interval(CI)0.696-0.863]and 0.741(95%CI 0.620-0.861)in the training set and external test set,respectively,and there were statistically significant differences compared with the radiomics model and the clinical model based on T1WI(P<0.05).Conclusion Deep learning model based on T2WI has a certain application value in preoperative noninvasive prediction of MVI status in HCC patients.

As a glucocorticoid drug with wide clinical application， triamcinolone acetonide can be administered by multiple routes， such as eye， nose， joint cavity， and skin， for the treatment of various local diseases such as arthritis， macular edema， rhinitis， and urticaria. As a drug with extremely low solubility in water， the dose form of triamcinolone acetonide is closely correlated with administration route and site. The dosage form of triamcinolone acetonide administered via injection（including joint cavity injection， vitreous injection， suprachoroidal injection， intramuscular injection） is mainly suspension， and the representative drugs include Kenalog-40®， Zilretta®， Triesence®， Xipere®， etc.； the dosage forms of nasal mucosal administration are mostly sprays， and the representative drug is Nasacort®； the dosage forms of oral mucosal administration are mostly patches， ointments and creams， and the representative drug is Oracort®； the dosage forms for transdermal administration are mostly ointments， creams and lotions， and the representative drugs include Trianex®， Teva-Triacomb®， etc. At present， the research on dosage forms of triamcinolone acetonide by various administration routes mainly focuses on the construction of delivery carriers， the addition of cosolvents or the use of new delivery tools.

Objective To observe the value of radiomics models based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced hepatobiliary phase(HBP)MRI for assessing clinical pathological stage of hepatic fibrosis(HF).Methods Data of 240 patients with pathologically/clinically diagnosed and clinical pathological staged HF who underwent Gd-EOB-DTPA enhanced MR examination were retrospectively analyzed.The liver-to-muscle signal intensity ratio(SIR1)and liver-to-spleen signal intensity ratio(SIR2)were measured based on HBP images.Radiomics features of HBP images were extracted and screened to construct radiomics models.The signal intensity ratio(SIR)-radiomics combined models were constructed based on SIR and radiomics signatures.Receiver operating characteristic(ROC)curves were drawn to evaluate the efficacy of each model for assessing clinical pathological stage of HF.Results The area under the curve(AUC)of SIR1 and SIR2 models for assessing clinical pathological stage of HF were 0.63-0.70 and 0.65-0.71,respectively.The most effective radiomics model for assessing HF,significant HF,advanced HF and early cirrhosis was support vector machine(SVM),SVM,light gradient boosting machine and K-nearest neighbor model,respectively,with the AUC in validation set of 0.87,0.82,0.81 and 0.80,respectively,while the AUC of SIR-radiomics combined models in validation set of 0.88,0.82,0.82 and 0.81,respectively.Conclusion The radiomics models based on Gd-EOB-DTPA enhanced HBP MRI were helpful for assessing clinical pathological stage of HF.Combining with HBP SIR could improve their efficacy.

Objective To investigate the clearance capacity of cardiac resident macrophages in post-sepsis and its underlying mechanism.Methods A mouse model of sepsis was established using cecum perforation ligation.Thirty male C57BL/6 mice(8 weeks old,weighing 20～25 g)were randomly and equally divided into a sham operation group(sham group)and a model group(sepsis group).Immunofluorescence assay was employed to label the cardiomyocytes and macrophages to observe the apoptosis of cardiomyocytes and the phagocytosis by cardiac resident macrophages.Cardiac resident macrophages were extracted for transcriptomic sequencing to determine the functional changes of the cells after sepsis.Cardiac resident macrophage cell lines were established at the cellular level and served as the normal group(RAC group),and the RAC cells treated with LPS were subjected as the sepsis group(RAC+LPS group).Then the differences in the ability to clear apoptotic cardiomyocytes between the 2 groups were observed.Then DQ-BSA-RED lysosomal activity detection probe,Lyso-Sensor yellow/bule dye,ELISA,and Western blotting were applied to detect the lysosomal function of cardiac resident macrophages,activity and expression of important lysosomal hydrolases,changes in contents and related subunits of vacuolar-type adenosine triphosphatases(V-ATPase).Results Compared with the sham group,the sepsis group had larger number of apoptotic cardiomyocytes(P＜0.05)and increased phagocytosis of cardiomyocytes by cardiac macrophages(P＜0.05).The results of transcriptomic sequencing revealed a significant dysfunction of lysosome-associated functions of cardiac-resident macrophages after sepsis.In in vitro experiments,the RAC+LPS group had a reduced fragmentation capacity of apoptotic cardiomyocytes,reduction in the intensity of yellow fluorescence of lysosomes(P＜0.05),and decrease in lysosomal hydrolase activity(P＜0.05)when compared with the RAC group.In addition,LPS treatment significantly decreased the activity and expression of V-ATPase and its major subunit ATP6V1A in cardiac resident macrophages(P＜0.05).Conclusion Cardiac resident macrophages show reduced clearance of apoptotic cardiomyocytes after sepsis,which may be related to a decrease in the activity of ATP6V1A,an important subunit of its lysosomal V-ATPase,and reduced activity of lysosomal hydrolases.

Objective:To analyze the molecular epidemiological characteristics of the Corona virus disease 2019 (COVID-19) cases in Shijiazhuang, which can reveal the origin of the outbreak and provide a scientific basis for COVID-19 prevention and control.Methods:From January 2 to January 8, 2021, a total of 404 samples from 170 COVID-19 cases were collected from the Shijiazhuang Fifth Hospital. The consensus sequence of 2019 novel Coronavirus(2019-nCoV) was obtained through multiplex polymerase chain reaction-based sequencing. The sequences of 170 COVID-19 cases were analyzed by the PANGOLIN, and the data were statistically analyzed by T-test.Results:Among the 404 COVID-19 samples, a total of 356 samples obtained high quality genome sequences (>95%,100×sequencing depth). The whole genome sequences of 170 COVID-19 cases were obtained by eliminating repeated samples. All 170 sequences were recognized as lineage B1.1 using PANGOLIN. The number of single nucleotide polymorphism arrange from 18-22 and most of the single nucleotide polymorphism were synonymous variants. All of 170 genomes could be classified into 48 sub-groups and most of the genomes were classified into 2 sub-groups (66 and 31, respectively).Conclusions:All cases in this study are likely originated from one imported case. The viruses have spread in the community for a long time and have mutated during the community transmission.

Objective:To investigate the value of preoperative prediction of Ki-67 expression status in breast cancer based on multi-phase enhanced MRI combined with clinical imaging characteristics prediction model.Methods:This study was retrospective. A total of 213 breast cancer patients who underwent surgical treatment at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between June 2016 and May 2017 were enrolled. All patients were female, aged 24-78 (51±10) years, and underwent routine breast MRI within 2 weeks prior to surgery. According to the different Ki-67 expression of postoperative pathological results, patients were divided into high expression group (Ki-67≥20%, 153 cases) and low expression group (Ki-67<20%, 60 cases). The radiomic features of breast cancer lesions were extracted from phase 2 (CE-2) and phase 7 (CE-7) images of dynamic contrast enhanced (DCE)-MRI, and all cases were divided into training and test sets according to the ratio of 7∶3. The radiomic features were first selected using ANOVA and Wilcoxon signed-rank test, followed by the least absolute shrinkage and selection operator method regression model. The same method of parameters selection was applied to clinical information and conventional imaging features [including gland classification, degree of background parenchymal enhancement, multifocal/multicentric, lesion location, lesion morphology, lesion long diameter, lesion short diameter, T 2WI signal characteristics, diffusion-weighted imaging (DWI) signal characteristics, apparent diffusion coefficient (ADC) values, time-signal intensity curve type, and axillary lymph nodes larger than 1 cm in short axis]. Support vector machine (SVM) was then used to construct prediction models for Ki-67 high and low expression states. The predictive performance of the models were evaluated using receiver operating characteristic (ROC) curves and area under cueve(AUC). Results:Totally 1 029 radiomic features were extracted from CE-2 and CE-7 images, respectively, and 9 and 7 best features were obtained after selection, respectively. And combining the two sets of features for a total of 16 features constituted the CE-2+CE-7 image best features. Five valuable parameters including lesion location, lesion short diameter, DWI signal characteristics, ADC values, and axillary lymph nodes larger than 1 cm in short axis, were selected from all clinical image features. The SVM prediction models obtained from the radiomic features of CE-2 and CE-7 images had a high AUC in predicting Ki-67 expression status (>0.70) in both the training set and the test set. The models were constructed by combining the CE-2, CE-7, and CE-2+CE-7 radiomic features with clinical imaging features, respectively, and the corresponding model performance in predicting Ki-67 expression status was improved compared with the models obtained by using the CE-2, CE-7, and CE-2+CE-7 radiomic features alone. The SVM prediction model obtained from CE-2+CE-7 radiomic features combined with clinical imaging features had the best prediction performance, with AUC of 0.895, accuracy of 84.6%, sensitivity of 87.9%, and specificity of 76.2% for predicting Ki-67 expression status in the training set and AUC of 0.822, accuracy of 70.3%, sensitivity of 76.1%, and specificity of 55.6% in test sets.Conclusion:The SVM prediction model based on DCE-MRI radiomic features can effectively predict Ki-67 expression status, and the combination of radiomic features and clinical imaging features can further improve the model prediction performance.

Primary biliary cholangitis (PBC) is an autoimmune liver disease manifesting as cholestasis and is often observed in the middle-aged and elderly women. About 50% of the patients have fatigue and itching, and 20% have depression or mood changes. In recent years, a number of studies have shown that the non-specific symptoms of patients with primary biliary cholangitis (PBC), such as fatigue, itching, and cognitive changes, are associated with the structural and functional changes of the central nervous system. Early identification of preclinical PBC patients through brain imaging changes may be one of the ways for the early diagnosis of this disease.