Cancer is serious endangers human life. After a long period of research and accumulation, people's understanding of cancer and the corresponding treatment methods are constantly developing. p53 is an important tumor suppressor gene. With the more in-depth understanding of the structure and function of p53, the more importance of this tumor suppressor gene is realized in the process of inhibiting tumor formation. MicroRNAs (miRNAs) are important regulatory molecules with a length of about 22nucleotides (nt), which belong to non-coding RNA and play an important role in the occurrence and development of tumors. miR-34 is currently considered to be a master regulator of tumor suppression. The positive feedback regulatory network formed by p53 and miR-34 can inhibit the growth and metastasis of tumor cells and inhibit tumor stem cells. This review focuses on the latest progress of p53/miR-34 regulatory network, and discusses its application in tumor diagnosis and treatment.

Background: Surgical site infection (SSI) is one of the most common types of health care–associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify high-risk patients and guide clinical intervention. However, most of these models had low accuracies. Objective: We aimed to provide a solution in the form of an Artificial intelligence–based Multimodal Risk Assessment Model for Surgical site infection (AMRAMS) for inpatients undergoing operations, using routinely collected clinical data. We internally and externally validated the discriminations of the models, which combined various machine learning and natural language processing techniques, and compared them with the National Nosocomial Infections Surveillance (NNIS) risk index. Methods: We retrieved inpatient records between January 1, 2014, and June 30, 2019, from the electronic medical record (EMR) system of Rui Jin Hospital, Luwan Branch, Shanghai, China. We used data from before July 1, 2018, as the development set for internal validation and the remaining data as the test set for external validation. We included patient demographics, preoperative lab results, and free-text preoperative notes as our features. We used word-embedding techniques to encode text information, and we trained the LASSO (least absolute shrinkage and selection operator) model, random forest model, gradient boosting decision tree (GBDT) model, convolutional neural network (CNN) model, and self-attention network model using the combined data. Surgeons manually scored the NNIS risk index values. Results: For internal bootstrapping validation, CNN yielded the highest mean area under the receiver operating characteristic curve (AUROC) of 0.889 (95% CI 0.886-0.892), and the paired-sample t test revealed statistically significant advantages as compared with other models (P<.001). The self-attention network yielded the second-highest mean AUROC of 0.882 (95% CI 0.878-0.886), but the AUROC was only numerically higher than the AUROC of the third-best model, GBDT with text embeddings (mean AUROC 0.881, 95% CI 0.878-0.884, P=.47). The AUROCs of LASSO, random forest, and GBDT models using text embeddings were statistically higher than the AUROCs of models not using text embeddings (P<.001). For external validation, the self-attention network yielded the highest AUROC of 0.879. CNN was the second-best model (AUROC 0.878), and GBDT with text embeddings was the third-best model (AUROC 0.872). The NNIS risk index scored by surgeons had an AUROC of 0.651. Conclusions: Our AMRAMS based on EMR data and deep learning methods—CNN and self-attention network—had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.

Multi-sensor data fusion is a technology to enable combining information from several sources in order to form a unified picture. Focusing on the indirect method, an attempt was made to build up a multi-sensor data fusion system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent monitoring system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The application case indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole

Background: Patient-focused evaluation of postoperative recover has been recognized as one of the most important concerns in postoperative medicine. Previous studies have shown that the Quality of Recovery-40 (QoR-40) Questionnaire can be used to accurately assess the quality of recovery from surgery. During thyroid surgery using intraoperative neuromonitoring (IONM) technology, the strategy of low dose of muscle relaxant, intubation of different endotracheal tubes and electrical stimulation on vocal cord are applied. Its still unknown if these performances would affect patients’ postoperative recovery in thyroid surgery patients. Methods: 82 patients were randomly assigned to the neuromonitoring group (NEURO Group) and the control group (CON Group). In the CON Group, rocuronium (0.6 mg / kg) was given for intubation and additional dose was injected if needed, while in the NEURO Group, only rocuronium (0.3 mg / kg) was given when induction. The primary outcome is the QoR-40 scores on postoperative day 1 (POD1) and postoperative day 3 (POD3). Other parameters, such as postoperative nausea or vomiting (PONV) and medical cost were also recorded. Results: One subject in each group was excluded, leaving 80 for analysis. In the NEURO Group, the global QoR-40 score, emotional state, physical comfort, physical independence and pain were significantly lower both on POD1 and POD3 (P<0.05). Patients in the NEURO Group had a higher incidence of PONV (P<0.05) and medical expense (P<0.05). Conclusions: After thyroidectomy, the patients using IONM suffer worse quality of recovery, more risk of PONV and increased medical expense.