We read the recent published paper in this journal of J Gastrointest Oncol by Zhang and colleagues entitled "Secondary colon cancer in patients with ulcerative colitis: a systematic review and meta-analysis" (1). They performed a systematic review and meta-analysis to assess the correlation between ulcerative colitis (UC) and colon cancer. We appreciate Zhang et al. (1) for the valuable study, however, after a careful learning of the literature, several limitations should be noticed.First, in the results section of the abstract, Zhang et al.(1) performed the meta-analysis by random-effect model because of statistical heterogeneity (Chi 2 =103.10; I 2 =90%; P<0.00001) and found that there were no significant differences between colon cancer in patients with UC and patients without colon carcinoma (Z =12.44; P<0.00001). However, we believe the interpretation of the results was false. There should be significant difference due to P<0.00001.Second, in the statistical methods section of this article, Zhang et al. (1) stated that the odds ratio (OR) was used as an effect size for dichotomous variables. Whereas, in this meta-analysis, the effect size actually was relative risk (RR) showed in figures 5,6 and the OR was not reported in the study. Therefore, we believe the irrelevant effect size depicted would lead to misunderstanding.

Hovenia acerba is a precious medicinal and edible tree. We assessed the genetic variation of H. acerba quality traits and conducted a comprehensive germplasm resource evaluation to provide a theoretical basis for breeding edible, medicinal, and edible/medicine combination varieties. We evaluated 31 H. acerba germplasm resources, including 12 infructescence and 8 fruit quality traits using correlation, principal component, and cluster analyses. The results showed that there were significant differences in all quality traits, with an average coefficient of variation greater than 0.20, an average genetic diversity greater than 1.80, and an average repeatability greater than 0.90. The average genetic variation and repeatability of quality traits in infructescence were higher than fruit. Infructescence K, Ca, Mn, Mg, and reducing sugar contents are important indicators in evaluating infructescence and fruit quality traits, and infructescence K, Mg, and reducing sugar contents are also quality innovation indices of H. acerba germplasms. Tannin, protein, and soluble sugar were the most suitable quality components for screening, followed by reducing sugar, starch, fat, total saponins, and total flavones. According to principal component factor scores and cluster analysis results, specific genotypes were selected as breeding materials for infructescence protein, tannin, flavone, reductive sugar, fruit tannin, fat, flavonoid, saponin, protein, and starch. The correlation analysis with environmental factors showed that the total amount of applied water could influence H. acerba infructescence and fruit quality. In conclusion, the variability of H. acerba germplasm resources was rich, and selection potential is large, which is beneficial to germplasm quality innovation and breeding.

We read the recent published paper in this journal of J Gastrointest Oncol by Chen and colleagues entitled "Improved sensitivity and positive predictive value of contrast-enhanced intraoperative ultrasound in colorectal cancer liver metastasis: a systematic review and meta-analysis" (1). They performed a systematic review and meta-analysis to assess the sensitivity and predictive value of contrast-enhanced intraoperative ultrasound (CE-IOUS) in colorectal cancer liver metastasis (CRLM) patients undergoing surgery. We appreciate Chen et al. (1) for the valuable study, however, after a careful learning of the literature, several limitations should be noticed.First, in the overall analyses of CE-IOUS section of the article, the summary receiver operating characteristics curve revealed a higher accuracy with area under the curve (AUC) 0.9753. The authors believed that the closer the AUC is to 1.0, the higher the sensitivity and predictive value of CE-IOUS and the more benefit. However, we believe that the interpretation of the results was false, as the AUC displayed the performance of CE-IOUS in the detection of CRLM not the accuracy, sensitivity, predictive value, and benefit. Furthermore, the same issues exist in the overall analyses of overall analyses of IOUS section of the article.Second, in the statistical analysis section of the article,

The application rate for sprinkler irrigation of water–fertilizer integration machines is an important technical parameter for efficient operation. If the value is too large, the equipment will produce runoff; if it is too small, the equipment will run too long and waste energy. Therefore, it is necessary to provide a feasible scientific and theoretical basis for developing a reasonable application rate. In this study, a mathematical model of soil infiltration for sprinkler irrigation with water and fertilizer integration machines was developed. Soil water accumulation time for different soil’s initial water content, bulk density, sprinkler application rate and soil texture were derived by the finite element method, and these data were used as a training database for the neural network. To make the neural network convenient for predicting the optimal application rate of sprinkler irrigation (the maximum application rate of sprinkler irrigation without runoff) in practice, the time of waterlogging, was multiplied by the optimal application rate of sprinkler irrigation to obtain the total irrigation volume. The optimal application rate of the sprinkler irrigation prediction model of radial basis function (RBF) neural network was constructed with total irrigation water, soil bulk density, initial water content and soil texture as inputs and compared with BP neural network and generalized regression neural network. The highest prediction accuracy of RBF neural network was obtained, and its average relative error was 0.11. To verify the accuracy of the RBF neural network application rate of sprinkler irrigation prediction model in real life, a sprinkler experiment was conducted in the laboratory of Guangzhou University, and the collected soil and lawn of Guangzhou University were used to simulate the actual environment. The results showed that the relative error between the RBF neural network prediction results and the actual values was generally around 10%, while for a total irrigation volume of 58 mm, the optimal application rate of sprinkler irrigation calculated with the model was 42 (mm/h), which can save 70% of irrigation time compared to the case of using the stable infiltration rate of soil as the application rate of sprinkler irrigation without water and fertilizer. Water and fertilizer losses were not observed. This indicates that the model proposed in this study is of practical value in determining the optimum application rate of sprinkler irrigation for water–fertilizer integration machines.