Prognostic Significance of C-reactive Protein-to-prealbumin Ratio in Patients with Esophageal Cancer

Background: The prognostic value of combination of C-reactive protein and prealbumin (CRP/PAlb) in esophageal cancer remains unclear. Methods: We enrolled 167 esophageal cancer patients who underwent curative esophagectomy. Univariate and multivariate analyses were performed to determine the prognostic significance of various markers, including CRP-to-albumin (CRP/Alb) ratio, modified Glasgow prognostic score, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index. Results: Receiver operating characteristic analysis revealed the optimal cut-off value of each inflammatory factor, and CRP/PAlb ratio had the greatest discriminative power in predicting recurrence-free survival (RFS) among the examined measures (AUC 0.668). The 5-year overall survival and RFS rates were significantly lower in patients with high CRP/PAlb ratio than in those with low CRP/PAlb ratio (P < 0.001, P = 0.001, respectively). In the univariate analysis, RFS was significantly worse in patients with low BMI, T2 or deeper tumor invasion, positive lymph node metastasis, positive venous invasion, high CRP/PAlb ratio, high CRP/Alb ratio, high NLR, and high LMR. Multivariate analysis revealed that CRP/PAlb, but not CRP/Alb, was an independent prognostic factor along with lymph node metastasis. Conclusion: CRP/PAlb ratio was useful for predicting the prognosis of esophageal cancer patients

Long-term results of a randomized controlled trial comparing neoadjuvant Adriamycin, cisplatin, and 5-fluorouracil vs docetaxel, cisplatin, and 5-fluorouracil followed by surgery for esophageal cancer (OGSG1003)

Sugimura, K, Yamasaki, M, Yasuda, T, et al. Long‐term results of a randomized controlled trial comparing neoadjuvant Adriamycin, cisplatin, and 5‐fluorouracil vs docetaxel, cisplatin, and 5‐fluorouracil followed by surgery for esophageal cancer (OGSG1003). Ann. Gastroenterol. Surg. 2020; 00: 1– 8. https://doi.org/10.1002/ags3.12388

Abstract Natural Language Analysis Using a Network Model- Modification Deciding Network-

We have developed an original analyzing method using the network structure called the MDN. The network is similar to that of a neural network. In the MDN, all of the modification candidates can be compared in parallel, and it can decide the most appropriate interpretation effectively. It allows high quality of natural language analysis, and high analyzing speed. In this paper we will describe Japanese sentence analysis using the MDN, and then describe discussions about the MDN, comparing with sequential analysis, neural networks, and interactive analysis.

Quadratic Unconstrained Binary Optimization for the Automotive Paint Shop Problem

The Binary Paint Shop Problem (BPSP) is a combinatorial optimization problem which draws inspiration from the automotive paint shop. Its binary nature, making it a good fit for Quadratic Unconstrained Binary Optimization (QUBO) solvers, has been well studied but its industrial applications are limited. In this paper, in order to expand the industrial applications, QUBO formulations for two generalizations of the BPSP, which are the Multi-Car Paint Shop Problem (MCPSP) and the Multi-Car Multi-Color Paint Shop Problem (MCMCPSP), are proposed. Given the multiple colors, the MCMCPSP is no longer natively binary which increases the problem size and introduces additional constraint factors in the QUBO formulation. Resulting QUBOs are solved using Scatter Search (SS). Furthermore, extensions of the SS that can exploit k-hot constrained structures within the formulations are proposed to compensate the additional complexity introduced by formulating non-binary problems into QUBO. Since no public benchmark database currently exists, random problem instances are generated. Viability of the proposed QUBO solving methods for the MCPSP and MCMCPSP, is highlighted through comparison with an integer-based Random Parallel Multi-start Tabu Search (RPMTS) and a greedy heuristic for the problems. The greedy heuristic has negligible computational requirements and therefore serves as a lower bound on the desired performance. The results for both problems show that better results can be obtained than the greedy heuristic and integer-based RPMTS, by using the novel k-hot extensions of the SS to solve the problems as QUBO