Correction to: Increasing targeting scope of adenosine base editors in mouse and rat embryos through fusion of TadA deaminase with Cas9 variants

In the original publication the grant number is incorrectly published. The correct grant number should be read as "17140901600". The corrected contents are provided in this correction article. This work was partially supported by grants from the National Natural Science Foundation of China (Nos. 81670470 and 81600149), a grant from the Shanghai Municipal Commission for Science and Technology (17140901600, 18411953500 and 15JC1400201) and a grant from National Key Research and Development Program (2016YFC0905100).

Diagnosis of pancreatic carcinoma based on combined measurement of multiple serum tumor markers using artificial neural network analysis / 中华医学杂志(英文版)

<p><b>BACKGROUND</b>Artificial neural network (ANN) has demonstrated the ability to assimilate information from multiple sources to enable the detection of subtle and complex patterns. In this research, we evaluated an ANN model in the diagnosis of pancreatic cancer using multiple serum markers.</p><p><b>METHODS</b>In this retrospective analysis, 913 serum specimens collected at the Department of General Surgery of Beijing Friendship Hospital were analyzed for carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA). The three tumor marker values were used as inputs into an ANN and randomized into a training set of 658 (70.31% were malignant) and a test set of the remaining 255 samples (70.69% were malignant). The samples were also evaluated using a Logistic regression (LR) model.</p><p><b>RESULTS</b>The ANN-derived composite index was superior to each of the serum tumor markers alone and the Logistic regression model. The areas under receiver operating characteristic curves (AUROC) was 0.905 (95% confidence Interval (CI) 0.868-0.942) for ANN, 0.812 (95% CI 0.762-0.863) for the Logistic regression model, 0.845 (95% CI 0.798-0.893) for CA19-9, 0.795 (95% CI 0.738-0.851) for CA125, and 0.800 (95% CI 0.746-0.854) for CEA. ANN analysis of multiple markers yielded a high level of diagnostic accuracy (83.53%) compared to LR (74.90%).</p><p><b>CONCLUSION</b>The performance of ANN model in the diagnosis of pancreatic cancer is better than the single tumor marker and LR model.</p>