Objective: This work aimed to investigate the relationships of the serum levels of IL-17 and TGF-β with the carcinogenesis and progression of colorectal cancer (CRC), as well as the clinical significance of these serum levels. Methods: Data of 30 healthy subjects, 59 patients with simple CRC and 44 CRC patients with postoperative (post-op) metastasis were recruited in this study. The patients were respectively divided into group A (30 healthy subjects as the control group), group B (59 CRC patients without distant metastasis after surgery), and group C (44 CRC patients with post-op metastasis). The patients in each group had a mean age of 53.8 ± 20.8, 62.0 ± 11.8, and 64.0 ± 15.7 years, respectively. All patients were confirmed by pathological diagnosis. The serum levels of IL-17 and TGF-β were measured by enzyme-linked immunosorbent assay. All quantitative data were analyzed using SPSS 13.0. Results: The IL-17 serum level was significantly higher in groups B and C than in group A. The preoperative (pre-op) serum level of IL-17 was significantly higher than the post-op serum level in group B (P<0.05). No significant difference was observed in the TGF-β serum levels between groups A and B, as well as between the pre-op and post-op serum levels in group B (P>0.05). However, the TGF-β serum level in group C was significantly higher than that in groups A and B (P<0.05). No significant correlation was observed in the serum level IL-17 or TGF-β between colon and rectum cancers in groups B and C. Conclusion: The serum level of IL-17 is significantly correlated with that of CRC. The serum level IL-17 increases with the aggravation of CRC and increased tumor burden. A strong correlation exists between the serum level of TGF-β and metastasis of CRC. Cytokine IL-17 and TGF-β may play an important role in the progression and metastasis of CRC.

Purpose/Significance To develop a secure single-cell RNA sequencing(scRNA-seq)classification method,which can enhance data analysis precision and ensure the security of sensitive information,and to promote the application of scRNA-seq technology in various fields.Method/Process The paper proposes a solution based on trusted execution environment(TEE).The training data is encrypted and transmitted to TEE.It is decrypted in a secure and isolated environment,while training the model to obtain the trained model parameters.Automated cell type i-dentification using neural networks(ACTINN)and support vector machine(SVM)are used for cell classification in both TEE and traditional plain-text environments.The results are compared and analyzed.Result/Conclusion The results show that the F1 score of the two classification models in TEE environment reaches 0.904 and 0.879,respectively,which is comparable to the performance in traditional plaintext environment.The secure ex-ecution environment provided by TEE has extremely limited impact on the accuracy and efficiency of the models.This is of great significance for see-king both secure and efficient data processing solutions in scenarios where sensitive or private data needs to be processed.

Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.

Objective:In general, patients with seropositive rheumatoid arthritis (RA) are considered to show an aggressive disease course. However, the relationship between the two subgroups in disease severity is controversial. Our study is aimed to compare the clinical characteristics and prognosis of double-seropositive and seronegative RA in China through a real-world large scale study.Methods:RA patients who met the 1987 American College of Rheumatology (ACR) classification criteria or the 2010 ACR/European Anti-Rheumatism Alliance RA classification criteria, and who attended the 10 hospitals across the country from September 2015 to January 2020, were enrolled. According to the serological status, patients were divided into 4 subgroups [rheumatoid factor (RF)(-) anti-cyclic citrullinated peptide (CCP) antibody (-), RF(+), RF(+) anti-CCP antibody(+), anti-CCP antibody(+)] and compared the disease characteristics and treatment response. One-way analysis of variance was used for measurement data that conformed to normal distribution, Kruskal-Wallis H test was used for measurement data that did not conform to normal distribution; paired t test was used for comparison before and after treatment within the group if the data was normally distributed else paired rank sum test was used; χ2 test was used for count data. Results:① A total of 2 461 patients were included, including 1 813 RF(+) anti-CCP antibody(+) patients (73.67%), 129 RF(+) patients (5.24%), 245 RF(-) anti-CCP antibody(-) patients (9.96%), 74 anti-CCP antibody(+) patients (11.13%). ② Regardless of the CCP status, RF(+) patients had an early age of onset [RF(-) anti-CCP antibody(-) (51±14) years old, anti-CCP antibody(+) (50±15) years old, RF(+) anti-CCP antibody(+) (48±14) years old, RF(+)(48±13) years old, F=3.003, P=0.029], longer disease duration [RF(-) anti-CCP antibody(-) 50 (20, 126) months, anti-CCP antibody(+) 60(24, 150) months, RF(+) anti-CCP antibody(+) 89(35, 179) months, RF(+) 83(25, 160) months, H=22.001, P<0.01], more joint swelling counts (SJC) [RF(-) anti-CCP antibody(-) 2(0, 6), Anti-CCP antibody(+) 2(0, 5), RF(+) anti-CCP antibody(+) 2(0, 7), RF(+) 2(0, 6), H=8.939, P=0.03] and tender joint counts (TJC) [RF(-) anti-CCP antibody(-) 3(0, 8), anti-CCP antibody(+) 2(0, 6), RF(+) anti-CCP antibody(+) 3(1, 9), RF(+) 2(0, 8), H=11.341, P=0.01] and the morning stiff time was longer [RF(-) anti-CCP antibody(-) 30(0, 60) min, anti-CCP antibody(+) 20(0, 60) min, RF(+) anti-CCP antibody(+) 30(10, 60) min, RF(+) 30(10, 60) min, H=13.32, P<0.01]; ESR [RF(-) anti-CCP antibody(-) 17(9, 38) mm/1 h, anti-CCP antibody(+) 20(10, 35) mm/1 h, RF(+) anti-CCP antibody(+) 26(14, 45) mm/1 h, RF(+) 28(14, 50) mm/1 h, H=37.084, P<0.01] and CRP [RF(-) anti-CCP antibody(-) 2.3 (0.8, 15.9) mm/L, Anti-CCP antibody(+) 2.7(0.7, 12.1) mm/L, RF(+) anti-CCP antibody(+) 5.2(1.3, 17.2) mm/L, RF (+) 5.2(0.9, 16.2) mm/L, H=22.141, P<0.01] of the RF(+)patients were significantly higher than RF(-) patients, and RF(+) patients had higher disease severity(DAS28-ESR) [RF(-) anti-CCP antibody(-) (4.0±1.8), anti-CCP antibody(+) (3.8±1.6), RF(+) anti-CCP antibody(+) (4.3±1.8), RF(+) (4.1±1.7), F=7.269, P<0.01]. ③ The RF(+) anti-CCP antibody(+) patients were divided into 4 subgroups, and it was found that RF-H anti-CCP antibody-L patients had higher disease severity [RF-H anti-CCP antibody-H 4.3(2.9, 5.6), RF-L anti-CCP antibody-L 4.5(3.0, 5.7), RF-H anti-CCP antibody-L 4.9(3.1, 6.2), RF-L anti-CCP antibody-H 2.8(1.8, 3.9), H=20.374, P<0.01]. ④ After 3-month follow up, the clinical characteristics of the four groups were improved, but there was no significant difference in the improvement of the four groups, indicating that the RF and anti-CCP antibody status did not affect the remission within 3 months. Conclusion:Among RA patients, the disease activity of RA patients is closely related to RF and the RF(+) patients have more severe disease than RF(-) patients. Patients with higher RF titer also have more severe disease than that of patients with low RF titer. After 3 months of medication treatment, the antibody status does not affect the disease remission rate.

OBJECTIVE：To evaluate the econo mics of pembrolizumab in the second-line treatment of advanced hepatocellular carcinoma in China. METHODS ：From the perspective of Chinese healthcare system ，a three-state PartSA model and Markov model were established ；the cost and utility for the second-line treatment of advanced hepatocellular carcinoma in China were compared between pembrolizumab and placebo. The circulation cycle of the model was 3 weeks and the study time limit was lifetime；one-way sensitivity analysis ，probability sensitivity analysis and scenario analysis were used to verify the robustness of the base-case analysis results. RESULTS ：PartSA results showed that the ICER for the second-line treatment of advanced hepato- cellular carcinoma with pembrolizumab was 1 266 846.18 yuan/QALY，which is far more than 1-3 times of China ’s per capita GDP in 2020. The results of one-way sensitivity analysis showed that the three parameters that had the greatest impact on ICER were the PFS status utility of the placebo group ，the PFS status utility of the pembrolizumab group ，and the cost of pembrolizumab. The results of probability sensitivity analysis verified the robustness of the base-case analysis. The scenario analysis showed that the treatment cost of pembrolizumab had dropped significantly when the charity donation of pembrolizumab was considered. Although it was still not economical ，ICER was close to 3 times of per capita GDP of China in 2020. When WTP threshold was 1 and 3 times of China ’s per capita GDP ，the economic prices of pabolizumab （100 mg）were 4 157.67 and 5 829.24 yuan，respectively. The results of Markov model were similar to those of PartSA model. CONCLUSIONS ：Under the WTP threshold of 1-3 times China ’s per capita GDP in 2020，pembrolizumab is not economical for second-line treatment of advanced hepatocellular carcinoma.