Objective:To explore the risk factors for early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH), and construct a clinical-radiomics combined model to predict HE after sICH.Methods:From April 2014 to September 2020, 339 patients with sICH who underwent plain CT scans in Radiology Department of our hospital were recruited. Patients were divided into HE group and non-HE group according to whether HE occurred (HE was defined as an increase in hematoma volume>33% or 6 mL on the follow-up CT within 24 h). The clinical data of non-HE group and HE group were compared, and multivariate Logistic regression analysis was used to detect independent risk factors for HE. The radiomics features were extracted from the regions of interest of the hematoma in the first CT scan images; the optimal radiomics features were selected using least absolute shrinkage and selection operator (LASSO) regression model and 10-fold cross-validation method, and then, the radiomics scores (R-score) were calculated; the risk factors for HE (clinical data) and R-score (radiomics data) were used to construct the clinical model, R-score model, and clinical-radiomics combined model; receiver operating characteristic (ROC) curve was performed to evaluate the prediction performance of clinical model, R-score model, and clinical-radiomics combined model; the best model was visualized as a nomogram and a calibration curve was drawn to evaluate the prediction accuracy of this model.Results:As compared with patients in the non-HE group, patients in the HE group had shorter time from sICH onset to first CT, higher percentage of patients with diabetes, lower platelet count, lower Glasgow Coma Scale (GCS) scores, and larger baseline hematoma volume in CT image, with significant differences ( P<0.05). Multivariate Logistic regression analysis showed that baseline hematoma volume ( OR=1.015, 95%CI: 1.000-1.030, P=0.046), GCS scores ( OR=0.914, 95%CI: 0.839-0.995, P=0.039), time from sICH onset to first CT ( OR=0.855, 95%CI: 0.741-0.987, P=0.032), and diabetes ( OR=0.522, 95%CI: 0.311-0.875, P=0.014) were independent risk factors for HE. By using LASSO regression and 10-fold cross-validation method, 20 optimal radiomics features were finally selected. The area under ROC curve of clinical model, R-score model, and clinical-radiomics combined model were 0.650, 0.860, and 0.870, respectively. The calibration curve showed that the prediction accuracy of clinical-radiomics combined model in early HE had good consistency with the actual occurrence probability. Conclusion:The clinical-radiomics combined model could effectively predict early HE with good calibration, which is helpful in individualized clinical assessment of risk of early HE in SICH patients.

Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively.The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

OBJECTIVE To prepare celastrol -loaded albumin nanoparticles （CLT-AN），and to investigate their activity against rheumatoid arthritis （RA）in vivo . METHODS CLT-AN was prepared by ultrasonic method . The formulation technology was optimized by single -factor test by taking particle size ，polydispersity index （PDI）and stability as indexes ，with the dosage of CLT ， the dosage of soybean oil and the ultrasonic power as factors . The physical and chemical properties of CLT -AN were investigated by transmission electron microscopy （TEM）and laser particle size analyzer ；in vitro stability and release profile were studied . A rat model of adjuvant -induced arthritis was constructed to investigate the effects of CLT -AN on joint swelling ，the levels of serum inflammatory factors [tumor necrosis factor α（TNF-α）and interleukin -1β（IL-1β）] and pathological state of joint tissue . RESULTS The optimized formulation was CLT 6.5 g，soybean oil 45 mg，ultrasonic power 490 W，ultrasonic time 8 min. CLT- AN prepared by the best formulation showed uniform and spherical morphology . Its particle size ，PDI，Zeta potential were （96.8± 1.1）nm，0.174±0.020，and（－18.6±1.7）mV，respectively. The encapsulation efficiency and drug -loading efficiency were （94.61±0.46）% and（2.42±0.21）%. There were no significant changes in particle size ，PDI，Zeta potential and encapsulation efficiency of CLT -AN within 5 days of storage at room temperature . CLT-AN was slowly released in vitro ，and the cumulative release reached 73.56% in 72 h. Compared with CLT ，CLT-AN could significantly inhibit the joint swelling of model rats ，reduced the levels of inflammatory factors TNF -α and IL -1β in serum ，and improved the pathological state of inflammatory joint tissue . CONCLUSIONS CLT-AN prepared by ultrasonic method has the appropriate particle size ，good stability ，significant sustained - release characteristics ，and excellent therapeutic efficacy against RA .

Objective To compare the epidemiological characteristics of COVID-19 in Guangzhou and Wenzhou, and evaluate the effectiveness of their prevention and control measures. Methods Data of COVID-19 cases reported in Guangzhou and Wenzhou as of 29 February, 2020 were collected. The incidence curves of COVID-19 in two cities were constructed. The real time reproduction number ( R t ) of COVID-19 in two cities was calculated respectively. Results A total of 346 and 465 confirmed COVID-19 cases were analysed in Guangzhou and Wenzhou, respectively. In two cities, most cases were aged 30-59 years (Guangzhou: 54.9%; Wenzhou: 70.3%). The incidence curve peaked on 27 January, 2020 in Guangzhou and on 26 January, 2020 in Wenzhou, then began to decline in both cities. The peaks of imported COVID-19 cases from Hubei occurred earlier than the peak of COVID-19 incidences in two cities, and the peak of imported cases from Hubei occurred earlier in Wenzhou than in Guangzhou. In early epidemic phase, imported cases were predominant in both cities, then the number of local cases increased and gradually took the dominance in Wenzhou. In Guangzhou, the imported cases was still predominant. Despite the different epidemic pattern, the R t and the number of COVID-19 cases declined after strict prevention and control measures were taken in Guangzhou and in Wenzhou. Conclusion The time and scale specific differences of imported COVID-19 resulted in different epidemic patterns in two cities, but the spread of the disease were effectively controlled after taking strict prevention and control measures.

Objective To assess the imported risk of COVID-19 in Guangdong province and its cities, and conduct early warning. Methods Data of reported COVID-19 cases and Baidu Migration Index of 21 cities in Guangdong province and other provinces of China as of February 25, 2020 were collected. The imported risk index of each city in Guangdong province were calculated, and then correlation analysis was performed between reported cases and the imported risk index to identify lag time. Finally, we classified the early warming levels of epidemic by imported risk index. Results A total of 1 347 confirmed cases were reported in Guangdong province, and 90.0% of the cases were clustered in the Pearl River Delta region. The average daily imported risk index of Guangdong was 44.03. Among the imported risk sources of each city, the highest risk of almost all cities came from Hubei province, except for Zhanjiang from Hainan province. In addition, the neighboring provinces of Guangdong province also had a greater impact. The correlation between the imported risk index with a lag of 4 days and the daily reported cases was the strongest (correlation coefficient: 0.73). The early warning base on cumulative 4-day risk of each city showed that Dongguan, Shenzhen, Zhongshan, Guangzhou, Foshan and Huizhou have high imported risks in the next 4 days, with imported risk indexes of 38.85, 21.59, 11.67, 11.25, 6.19 and 5.92, and the highest risk still comes from Hubei province. Conclusions Cities with a large number of migrants in Guangdong province have a higher risk of import. Hubei province and neighboring provinces in Guangdong province are the main source of the imported risk. Each city must strengthen the health management of migrants in high-risk provinces and reduce the imported risk of Guangdong province.