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The tumor immune infiltrate, as recently evaluated with the immunoscore methodology, has been reported to be related to colorectal cancer (CRC) progression. Nevertheless, results varied from different studies. A meta‐analysis was conducted to solve this problem. We collected data from included studies to evaluate the prognostic role of immunoscore in CRC patients on overall survival (OS) and disease‐free survival (DFS). MEDLINE, EMBASE, and Cochrane libraries were searched through 30 June 2018. Hazard ratio (HR) with 95% confidence intervals (95% CI) was pooled using a random‐effects model for OS and a fixed‐effects model for DFS. Finally, eight studies (involving 4689 CRC cases) were identified as eligible publications. The results of the meta‐analysis showed that low immunoscore was significantly correlated with poor OS (HR = 1.74, 95% CI: 1.43‐2.13) and DFS (HR = 1.82, 95% CI: 1.64‐2.03). The findings from most subgroup analyses were consistent with those from the overall analysis. The immunoscore could be a useful prognostic marker in patients with CRC. It is necessary to evaluate immunological markers in international multicenter studies. The meta‐analysis involving a total of 4689 colorectal cancer cases from eight studies. The immunoscore could be a useful prognostic marker in patients with colorectal cancer. [ABSTRACT FROM AUTHOR]

The equal-area (EA) method is studied with respect to its applicability to a wide range of phase equilibrium scenarios for pure fluids and binary mixtures. The study covers vapor-liquid equilibrium (VLE), liquid-liquid equilibrium (LLE), solid-liquid equilibrium (SLE) and their crossover transitions. The thermodynamic models studied include equation of state, activity coefficient and solid solubility models and their combinations. The performance of the EA method for chain molecules, at very low temperature and nearby the critical point is also investigated. We conclude that the EA method is very reliable and efficient and has a number of advantages over the conventional method. Finally, we apply the EA method to the regression of the model parameters to demonstrate its attractive application. © 2011 American Institute of Chemical Engineers AIChE J, 2012 [ABSTRACT FROM AUTHOR]