A CNN-Based Method for Heavy-Metal Ion Detection

Data processing is an essential component of heavy-metal ion detection. Most of the research now uses a conventional data-processing approach, which is inefficient and time-consuming. The development of an efficient and accurate automatic measurement method for heavy-metal ions has practical implications. This paper proposes a CNN-based heavy-metal ion detection system, which can automatically, accurately, and efficiently detect the type and concentration of heavy-metal ions. First, we used square-wave voltammetry to collect data from heavy-metal ion solutions. For this purpose, a portable electrochemical constant potential instrument was designed for data acquisition. Next, a dataset of 1200 samples was created after data preprocessing and data expansion. Finally, we designed a CNN-based detection network, called HMID-NET. HMID-NET consists of a backbone and two branch networks that simultaneously detect the type and concentration of the ions in the solution. The results of the assay on 12 sets of solutions with different ionic species and concentrations showed that the proposed HMID-NET algorithm ultimately obtained a classification accuracy of 99.99% and a mean relative error of 8.85% in terms of the concentration

Development and validation of a nomogram based on preoperative variables for predicting recurrence‐free survival in stage IA lung adenocarcinoma

Abstract Background This study aimed to establish a nomogram for predicting risk of recurrence and provide a model for decision‐making between lobectomy and sublobar resection in patients with stage IA lung adenocarcinoma. Methods Patients diagnosed with stage IA lung adenocarcinoma (LUAD) between December 2010 and October 2018 from Cancer Hospital Chinese Academy of Medical Sciences were included. Patients were randomly assigned to training and validation cohorts, accounting for 70% and 30% of the total cases, respectively. We collected laboratory variables before surgery. Univariate and multivariate analyses were performed in the training cohort to identify variables significantly associated with recurrence‐free survival (RFS) which were subsequently used to construct a nomogram. Validation was conducted in both cohorts. A receiver operating characteristic curve was used to determine the optional cutoff values of the scores calculated from the nomogram. Patients were then divided into low‐ and high‐risk groups. Survival was performed to determine if the nomogram could guide the operation method. Results A total of 543 patients were included in this study. Gender, albumin level, carcinoembryonic antigen level and cytokeratin‐19‐fragment level were included in the nomogram. In both cohorts, the nomogram stratified the patients into high‐ and low‐risk groups in terms of RFS. In particular, there was a significant difference in RFS between lobectomy and sublobar resection in the high‐risk group. Conclusions Gender, albumin level, carcinoembryonic antigen level and cytokeratin‐19‐fragment level are valuable markers in predicting recurrence and can guide surgical practice in patients with stage IA LUAD