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Meng-Xin Tian,1,2,* Liu-Ping Luo,3,4,* Wei-Ren Liu,1,2,* Wei Deng,5 Jia-Cheng Yin,1,2 Lei Jin,1,2 Xi-Fei Jiang,1,2 Yu-Fu Zhou,1,2 Wei-Feng Qu,1,2 Zheng Tang,1,2 Han Wang,1,2 Chen-Yang Tao,1,2 Yuan Fang,1,2 Shuang-Jian Qiu,1,2 Jian Zhou,1–2,6 Jing-Feng Liu,3,4 Jia Fan,1–2,6 Ying-Hong Shi1,21Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 2Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Shanghai, People’s Republic of China; 3Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 4The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5Department of Health Statistics and Social Medicine, Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, People‘s Republic of China; 6Institutes of Biomedical Sciences, Fudan University, Shanghai, People‘s Republic of China*These authors contributed equally to this work Purpose: To develop and validate a decision aid to help make individualized estimates of tumor recurrence for patients with resected combined hepatocellular cholangiocarcinoma (CHC).Patients and methods: Risk factors of recurrence were identified in the derivation cohort of 208 patients who underwent liver resection between 1995 and 2014 at Zhongshan Hospital to develop a prediction score. The model was subsequently validated in an external cohort of 101 CHC patients using the C concordance statistic and net reclassification index (NRI).Results: On multivariate analysis, five independent predictors associated with tumor recurrence were identified, including sex, γ-glutamyl transferase, macrovascular invasion, hilar lymphoid metastasis and adjuvant transcatheter arterial chemoembolization. The prediction score was constructed using these 5 variables, with scores ranging from 0 to 5. A patient with a score of 0 had a predicted 1- and 5-year recurrence risk of 11.1% and 22.2%, respectively. In the validation cohort, the NRIs of prediction score vs American Joint Committee on Cancer 7th, TNM staging system at 1-year and 5-year were 0.185 (95% CI, 0.090–0.279, P

Additional file 1.

Supplemental Digital Content is available in the text, Background: Dyslipidemia is a common disorder in systemic lupus erythematosus (SLE) patients. It is still inconclusive whether antimalarial drugs could affect the serum lipids in SLE patients, therefore we conducted a systematic review and meta-analysis of available data to address this issue. Methods: We comprehensively searched the databases of PubMed, EMBASE and Cochrane Library from date of inception to Sep 2018 for both randomized controlled trials (RCTs) and observational studies. Review Manager 5.3 software was used for analysis. We performed meta-analysis using random-effects model and weighted the mean difference (WMD) and its 95% confidence interval (CI). The Q test was used to assess the presence of heterogeneity and the I2 index was used to quantify the extent of heterogeneity. Results: In total, 8 studies met our selection criteria including 2 RCTs, 2 cohort studies, and 4 case-control studies. There were 717 patients (336 patients in CQ (chloroquine) or HCQ (hydroxychloroquine) group, and 381 patients in control group (SLE patients without the therapy of AM)). Compared with the control group, TC, TG, LDL-C, VLDL-C were associated with a significant decrease, respectively (WMD = −21.40 mg/dL, 95% CI −27.62 to −15.18, P

Table S1. The relationship between TRIS and clinicopathological variables. Table S2. Comparison of prognostic performance among postoperative variables and ICPI. Table S3. Comparison of prognostic performance among HCC staging systems and ICPI. Table S4. Components of 7 staging systems for hepatocellular carcinoma. Table S5. Antibody sources and staining conditions. (DOCX 28 kb)

Figure S1. Study flowchart. Figure S2. Digital image analyzed using the image software (Image-pro plus 6.0), with tissue represented in yellow and stained cells represented in red. Figure S3. Immunohistochemistry expression pattern of 14 immune infiltrations in tumor and adjacent liver tissues, including CD3, CD4, CD8, CD14, CD20, CD27, CD45RA, CD45RO, CD57, CD66b, CD68, CD103, CXCR5, and PD1. Figure S4. X-tile plots of ICPI in the training cohort automatically selecting the optimum cut point according to the highest Ď 2 value defined by the Kaplan-Meier survival analysis and log-rank test. Figure S5. Kaplan-Meier survival curves of the training cohort categorized by different staging systems. Figure S6. (A) Nomogram for predicting the survival probability in HCC patients. (B) Calibration of the predictive models at 1, 3, and 5â years in the derivation and validation cohorts. Figure S7. The correlation between TRIS and the density of intratumoral immune features, including CD4+, CD20+, CD45RO+, and CD45RA+ cells. (DOCX 5565 kb)

Supplementary methods. (DOCX 40 kb)