Application of AI and IoT in Clinical Medicine: Summary and Challenges.

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

Dermcidin as a novel binding protein of lncRNA STCAT3 and its effect on prognosis in gastric cancer.

Long non­coding RNAs (lncRNAs) perform distinct biological functions by regulating gene expression through various molecular mechanisms under normal physiological and pathological conditions. However, the function of the stomach cancer­associated transcript­3 (STCAT3) lncRNA, including its prognostic significance and role as a binding protein in gastric cancer (GC), remain unclear. In the present study, 56 potential binding proteins of STCAT3 were screened using through mass spectrometry and bioinformatics analysis. Among these, dermcidin, GAPDH, annexin, calmodulin­like protein, cathepsin­D and suprabasin were demonstrated to be candidate binding proteins using a literature search. RNA­protein interaction prediction was used to confirm these six proteins. Finally, dermcidin was identified as the binding protein of STCAT3 by comparing the mRNA and protein levels of the candidate genes and their correlations with STCAT3 in plasmid­transfected BGC­823 GC cell lines, as well as by validating the interplay between dermcidin and STCAT3 in other GC cell lines. Immunohistochemical analysis of tissues from 98 patients with GC further confirmed the interaction between dermcidin and STCAT3. The results of the present study also revealed that STCAT3 and dermcidin and independent predictors of overall survival in patients with GC. Furthermore STCAT3 and dermcidin are positively correlated with lymph node metastasis and tumor/node/metastasis score. In summary, the present study suggests that dermcidin is a novel binding protein of lncRNA STCAT3, which serves an important role in the progress and clinical outcome of GC.