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Xu, Hui,Zhang, Xiaopeng,Wu, Zhijun,Feng, Ying,Zhang, Cheng,Xie, Minmin,Yang, Yahui,Zhang, Yi,Feng, Chong,Ma, Tai The Korean Gastric Cancer Association 2021 Journal of gastric cancer Vol.21 No.3
Purpose: While several prognostic models for the stratification of death risk have been developed for patients with advanced gastric cancer receiving first-line chemotherapy, they have seldom been tested in the Chinese population. This study investigated the performance of these models and identified the optimal tools for Chinese patients. Materials and Methods: Patients diagnosed with metastatic or recurrent gastric adenocarcinoma who received first-line chemotherapy were eligible for inclusion in the validation cohort. Their clinical data and survival outcomes were retrieved and documented. Time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive ability of the models. Kaplan-Meier curves were plotted for patients in different risk groups divided by 7 published stratification tools. Log-rank tests with pairwise comparisons were used to compare survival differences. Results: The analysis included a total of 346 patients with metastatic or recurrent disease. The median overall survival time was 11.9 months. The patients were different into different risk groups according to the prognostic stratification models, which showed variability in distinguishing mortality risk in these patients. The model proposed by Kim et al. showed relative higher predicting abilities compared to the other models, with the highest χ<sup>2</sup> (25.8) value in log-rank tests across subgroups, and areas under the curve values at 6, 12, and 24 months of 0.65 (95% confidence interval [CI]: 0.59-0.72), 0.60 (0.54-0.65), and 0.63 (0.56-0.69), respectively. Conclusions: Among existing prognostic tools, the models constructed by Kim et al., which incorporated performance status score, neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, and tumor differentiation, were more effective in stratifying Chinese patients with gastric cancer receiving first-line chemotherapy.
Feiwei Zhang,Dairong Qiao,Hui Xu,Chong Liao,Shilin Li,Yi Cao 한국미생물학회 2009 The journal of microbiology Vol.47 No.3
Xylose reductase (XR) is a key enzyme in xylose metabolism because it catalyzes the reduction of xylose to xylitol. In order to study the characteristics of XR from Candida tropicalis SCTCC 300249, its XR gene (xyl1) was cloned and expressed in Escherichia coli BL21 (DE3). The fusion protein was purified effectively by Ni2+-chelating chromatography, and the kinetics of the recombinant XR was investigated. The Km values of the C. tropicalis XR for NADPH and NADH were 45.5 µM and 161.9 µM, respectively, which demonstrated that this XR had dual coenzyme specificity. Moreover, this XR showed the highest catalytic efficiency (kcat=1.44×104 min-1) for xylose among the characterized aldose reductases. Batch fermentation was performed with Saccharomyces serivisiae W303-1A:pYES2XR, and resulted in 7.63 g/L cell mass, 93.67 g/L xylitol, and 2.34 g/L·h xylitol productivity. This XR coupled with its dual coenzyme specificity, high activity, and catalytic efficiency proved its utility in in vitro xylitol production.
Zhang, Cheng,Xie, Minmin,Zhang, Yi,Zhang, Xiaopeng,Feng, Chong,Wu, Zhijun,Feng, Ying,Yang, Yahui,Xu, Hui,Ma, Tai The Korean Gastric Cancer Association 2022 Journal of gastric cancer Vol.22 No.2
Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.