Natural killer (NK) cells play critical roles in immune surveillance and homeostasis maintenance through cytotoxicity and cytokine release. The ability of NK cells to eliminate target cells without the need for prior antigen recognition or antibody involvement has drawn considerable attention for translational and clinical applications. As clinical applications continue to broaden, interest in NK cell assays has been growing markedly. This review provides an in-depth discussion of current clinical applications of NK cell assays, including NK cell phenotype, frequency, and functional activity assessments. This review further highlights the diagnostic potential of these assays in terms of hemophagocytic lymphohistiocytosis and potential NK cell biomarker candidates in diverse pathological contexts, such as cancer, infection, autoimmune diseases, and other diseases. By integrating clinical insights with technological advancements, NK cell assays can serve as valuable tools for disease diagnosis, prognosis, and therapeutic monitoring.

Augmented reality (AR) is a technology derived from roots such as virtual reality (VR), and it is a technology that combines the real world and the virtual environment by superimposing virtual images on the real world and interacting in real time.In order to implement AR, it is a real-world tracking system, a graphic system that accurately combines the virtual world generated by processing virtual information with the image of the real world to be shown to the user, and a display system that visualizes the result generated through the system. User displays are marketed in the form of glass or head-mounted display. In the aviation field, head-up display, which can assist in aircraft control, has been put into practical use and AR can be used for aircraft maintenance and training. In the medical field, it has been developed and tested in the form of a surgical planning platform that increases the accuracy of surgery. It can also be used for patient evacuation in the field of aeronautical medicine.AR is a promising technology in the future, and is expected to develop by converging with similar technologies such as VR and artificial intelligence.

The basic concept of virtual reality (VR) means ‘an artificial environment that is similar to reality but is not real.’ In general, virtual reality refers to a technology that goes beyond simply implementing a virtual space and directly affects the user's five senses to enable a spatial and temporal experience close to reality. Customer adoption of virtual reality technology is still in its infancy, but as innovation continues to strengthen, more businesses are exploring virtual reality. Several fields have already begun to benefit from the use of virtual reality. Specifically, advancements are being made in games, entertainment, medicine and education, It also enables aeromedical training using simulation techniques. Despite the bright future outlook, there are still problems that need to be resolved, such as high costs and lack of good content. In this article, I will introduce VR and consider its specific use cases in the field of aviation medicine.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Purpose: The diagnosis and management of cardiovascular diseases are important for pilots, as well as the assessment of workload. Heart rate variability (HRV) can be evaluated from electrocardiography (ECG) signals during flight phases to assess the activation of the autonomic nervous system. Methods: In this study, continuous ECG activity was recorded of one pilot who flied as a pilot flying during a 4-hour long round trip using wearable ECG machine and was analyzed with MATLAB (R2020b ver. 9.9, The Mathworks Inc.). Total flight was divided into five phases: preflight, take off, cruise, landing, and postflight. Results: Mean heart rate (HR) was lowest in the postflight phase (76 bpm), and highest in the landing phase (86 bpm). Landing phase showed the highest values in standard deviation of NN interval (59.3 ms), triangular index (11.7), and triangular interpolation of NN interval (195 ms), while the postflight phase had highest root mean square of successive difference (20.5 ms) and proportion of successive RR interval (3.4 ms). As for frequency-domain metrics, the landing phase had the highest lowfrequency/high-frequency ratio of 5.33. Among the non-linear HRV measures, the landing phase presented the lowest SD1/SD2 ratio (0.15). Conclusion We observed the relative increase of mean HR and change of HRV in the landing phase, indicating elevated sympathetic nervous tone. Further studies should be considered to evaluate specific changes of ECG signals in flight phases and confirm the clinical use of the MATLAB signal analysis tools.

Blockchain is a type of distributed database managed through multiple network transaction details (P2P, Peer to Peer), and transaction information is stored on multiple computers (nodes) connected to the blockchain network instead of storing it in one server. It is an algorithm that binds transaction details to form blocks, connects multiple blocks like a chain, and then copies and distributes them by a large number of people. In the case of the aviation industry, it is worth making full use of blockchain technology to revolutionize existing systems and business processes in terms of cost and transaction transparency, so blockchain can become one of the innovative technologies that will change the paradigm of the aviation industry. As the blockchain market is still in an immature stage and the development of technology is expected in the future, it is necessary to prepare support measures to lead and preempt the global blockchain market in the future through development support and cooperation that can be actively used in the field.

Big data is a technology that processes a large amount of structured and unstructured data sets that are beyond the ability to manage data with commonly used data analysis methods. According to the amount and type of data is showing explosive changes, and the amount and type of data in the industry are also increasing remarkably. Now, how to extract meaningful information from the exploding data from those of data bunches and use it as valuable information is becoming a social task. In the existing traditional data, individual data itself was important, but big data must deal with a large amount of data and judge it from an overall perspective, so innovation of the existing data judgment method is needed. Big data has a common feature that can be called “3V”: data volume, variety and velocity, because it is significantly different from existing traditional data handling method. Since the nature of big data has changed to the extent that it is impossible to smoothly collect and process it with existing technologies, various advanced big data processing technologies are needed to extract value. Big data is not a single technical item, but comprehensive technology in which various underlying technologies are fused.