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.

Carisbamate is an antiepileptic drug and it also has broad neuroprotective activity and anticonvulsant reaction. In this study, a liquid chromatography-quadrupole time-of-flight mass spectrometric (LC-qTOF-MS) method was developed and applied for the determination of carisbamate in rat plasma to support in vitro and in vivo studies. A quadratic regression (weighted 1/concentration2), with an equation y = ax2 + bx + c, was used to fit calibration curves over the concentration range from 9.05 to 6,600 ng/mL for carisbamate in rat plasma. Preclinical in vitro and in vivo studies of carisbamate have been studied through the developed bioanalytical method. Based on these study results, human pharmacokinetic (PK) profile has been predicted using physiologically based pharmacokinetic (PBPK) modeling. The PBPK model was optimized and validated by using the in vitro and in vivo data. The human PK of carisbamate after oral dosing of 750 mg was simulated by using this validated PBPK model. The human PK parameters and profiles predicted from the validated PBPK model were similar to the clinical data. This PBPK model developed from the preclinical data for carisbamate would be useful for predicting the PK of carisbamate in various clinical settings.

Carisbamate is an antiepileptic drug and it also has broad neuroprotective activity and anticonvulsant reaction. In this study, a liquid chromatography-quadrupole time-of-flight mass spectrometric (LC-qTOF-MS) method was developed and applied for the determination of carisbamate in rat plasma to support in vitro and in vivo studies. A quadratic regression (weighted 1/concentration2), with an equation y = ax2 + bx + c, was used to fit calibration curves over the concentration range from 9.05 to 6,600 ng/mL for carisbamate in rat plasma. Preclinical in vitro and in vivo studies of carisbamate have been studied through the developed bioanalytical method. Based on these study results, human pharmacokinetic (PK) profile has been predicted using physiologically based pharmacokinetic (PBPK) modeling. The PBPK model was optimized and validated by using the in vitro and in vivo data. The human PK of carisbamate after oral dosing of 750 mg was simulated by using this validated PBPK model. The human PK parameters and profiles predicted from the validated PBPK model were similar to the clinical data. This PBPK model developed from the preclinical data for carisbamate would be useful for predicting the PK of carisbamate in various clinical settings.

Human Q fever, a zoonosis caused by Coxiella burnetii, presents with diverse clinical manifestations ranging from mild self-limited febrile illnesses to life-threatening complications such as endocarditis or vascular infection. Although acute Q fever is a benign illness with a low mortality rate, a large-scale outbreak of Q fever in the Netherlands led to concerns about the possibility of blood transfusion-related transmission or obstetric complications in pregnant women. Furthermore, a small minority (< 5%) of patients with asymptomatic or symptomatic infection progress to chronic Q fever. Chronic Q fever is fatal in 5–50% of patients if left untreated. In South Korea, Q fever in humans was designated as a notifiable infectious disease in 2006, and the number of Q fever cases has increased sharply since 2015. Nonetheless, it is still considered a neglected and under-recognized infectious disease. In this review, recent trends of human and animal Q fever in South Korea, and public health concerns regarding Q fever outbreaks are reviewed, and we consider how a One Health approach could be applied as a preventive measure to prepare for zoonotic Q fever outbreaks.