Objective: Ultradian rhythms are biological rhythms with periods of a few seconds to a few hours. Along with circadian rhythms, ultradian rhythms influence human physiology. However, such rhythms have not been studied as intensively as circadian rhythms. This study aimed to identify ultradian rest-activity rhythms induced by the dopamine D2/D3 agonist quinpirole in mice. Methods: We used 10 mice from the Institute of Cancer Research. Quinpirole was administered at a dose of 0.5 mg/kg.We assessed free rest-activity using infrared detectors and conducted wavelet analysis to measure the period and its variation. We also used the paired t test to compare ultradian rhythm patterns. Results: Quinpirole did not significantly change total 24-hour locomotor activity (p = 0.065). However, it significantly increased locomotor activity during the dark phase (p = 0.001) and decreased it during the light phase (p = 0.016).In the continuous wavelet transform analysis, the mean period was 5.618 hours before quinpirole injection and 4.523 hours after injection. The period showed a significant decrease (p = 0.040), while the variation remained relatively consistent before and after quinpirole injection. Conclusion This study demonstrated ultradian rest-activity rhythms induced by quinpirole using wavelet analysis.Quinpirole-induced ultradian rhythms exhibited rapid oscillations with shortened periods and increased activity during the dark phase. To better understand these changes in ultradian rhythms caused by quinpirole, it is essential to compare them with the effects of other psychopharmacological agents. Furthermore, investigating the pharmacological impact on ultradian rest-activity rhythms may have valuable applications in clinical studies.

Objective: Ultradian rhythms are biological rhythms with periods of a few seconds to a few hours. Along with circadian rhythms, ultradian rhythms influence human physiology. However, such rhythms have not been studied as intensively as circadian rhythms. This study aimed to identify ultradian rest-activity rhythms induced by the dopamine D2/D3 agonist quinpirole in mice. Methods: We used 10 mice from the Institute of Cancer Research. Quinpirole was administered at a dose of 0.5 mg/kg.We assessed free rest-activity using infrared detectors and conducted wavelet analysis to measure the period and its variation. We also used the paired t test to compare ultradian rhythm patterns. Results: Quinpirole did not significantly change total 24-hour locomotor activity (p = 0.065). However, it significantly increased locomotor activity during the dark phase (p = 0.001) and decreased it during the light phase (p = 0.016).In the continuous wavelet transform analysis, the mean period was 5.618 hours before quinpirole injection and 4.523 hours after injection. The period showed a significant decrease (p = 0.040), while the variation remained relatively consistent before and after quinpirole injection. Conclusion This study demonstrated ultradian rest-activity rhythms induced by quinpirole using wavelet analysis.Quinpirole-induced ultradian rhythms exhibited rapid oscillations with shortened periods and increased activity during the dark phase. To better understand these changes in ultradian rhythms caused by quinpirole, it is essential to compare them with the effects of other psychopharmacological agents. Furthermore, investigating the pharmacological impact on ultradian rest-activity rhythms may have valuable applications in clinical studies.

Objective: Ultradian rhythms are biological rhythms with periods of a few seconds to a few hours. Along with circadian rhythms, ultradian rhythms influence human physiology. However, such rhythms have not been studied as intensively as circadian rhythms. This study aimed to identify ultradian rest-activity rhythms induced by the dopamine D2/D3 agonist quinpirole in mice. Methods: We used 10 mice from the Institute of Cancer Research. Quinpirole was administered at a dose of 0.5 mg/kg.We assessed free rest-activity using infrared detectors and conducted wavelet analysis to measure the period and its variation. We also used the paired t test to compare ultradian rhythm patterns. Results: Quinpirole did not significantly change total 24-hour locomotor activity (p = 0.065). However, it significantly increased locomotor activity during the dark phase (p = 0.001) and decreased it during the light phase (p = 0.016).In the continuous wavelet transform analysis, the mean period was 5.618 hours before quinpirole injection and 4.523 hours after injection. The period showed a significant decrease (p = 0.040), while the variation remained relatively consistent before and after quinpirole injection. Conclusion This study demonstrated ultradian rest-activity rhythms induced by quinpirole using wavelet analysis.Quinpirole-induced ultradian rhythms exhibited rapid oscillations with shortened periods and increased activity during the dark phase. To better understand these changes in ultradian rhythms caused by quinpirole, it is essential to compare them with the effects of other psychopharmacological agents. Furthermore, investigating the pharmacological impact on ultradian rest-activity rhythms may have valuable applications in clinical studies.

Objective: Ultradian rhythms are biological rhythms with periods of a few seconds to a few hours. Along with circadian rhythms, ultradian rhythms influence human physiology. However, such rhythms have not been studied as intensively as circadian rhythms. This study aimed to identify ultradian rest-activity rhythms induced by the dopamine D2/D3 agonist quinpirole in mice. Methods: We used 10 mice from the Institute of Cancer Research. Quinpirole was administered at a dose of 0.5 mg/kg.We assessed free rest-activity using infrared detectors and conducted wavelet analysis to measure the period and its variation. We also used the paired t test to compare ultradian rhythm patterns. Results: Quinpirole did not significantly change total 24-hour locomotor activity (p = 0.065). However, it significantly increased locomotor activity during the dark phase (p = 0.001) and decreased it during the light phase (p = 0.016).In the continuous wavelet transform analysis, the mean period was 5.618 hours before quinpirole injection and 4.523 hours after injection. The period showed a significant decrease (p = 0.040), while the variation remained relatively consistent before and after quinpirole injection. Conclusion This study demonstrated ultradian rest-activity rhythms induced by quinpirole using wavelet analysis.Quinpirole-induced ultradian rhythms exhibited rapid oscillations with shortened periods and increased activity during the dark phase. To better understand these changes in ultradian rhythms caused by quinpirole, it is essential to compare them with the effects of other psychopharmacological agents. Furthermore, investigating the pharmacological impact on ultradian rest-activity rhythms may have valuable applications in clinical studies.

Objective: Even though studies using machine learning on sleep-wake states have been performed, studies in various conditions are still necessary. This study aimed to examine the performance of the prediction model of locomotor activities on sleep-wake states using machine learning algorithms. Methods: The processed data using moving average of locomotor activities were used as predicting features. The sleep-wake states were used as true labels. The prediction models were established by machine learning classifiers such as support vector machine with radial basis function (SVM-RBF), linear discriminant analysis (LDA), naïve Bayes, and random forest (RF). The prediction model was evaluated by a six-fold cross validation. Results: The SVM-RBF and RF showed acceptable performance within a window of moving average from 480 to 1,200 seconds. The highest accuracy (0.869) was shown by the RF at the interval of 480 seconds. Meanwhile, the highest area under the curve (0.939) was shown by LDA at the interval of 870 seconds. Conclusion This study suggested that the prediction model on sleep-wake state using machine learning could show an improvement of the model performance when using moving average with raw data. The prediction model using locomotor activity can be useful in research on sleep-wake state.

Objective: Dysphagia is a common clinical condition characterized by difficulty in swallowing. It is sub-classified into oropharyngeal dysphagia, which refers to problems in the mouth and pharynx, and esophageal dysphagia, which refers to problems in the esophageal body and esophagogastric junction. Dysphagia can have a significant negative impact one’s physical health and quality of life as its severity increases. Therefore, proper assessment and management of dysphagia are critical for improving swallowing function and preventing complications. Thus a guideline was developed to provide evidence-based recommendations for assessment and management in patients with dysphagia. Methods: Nineteen key questions on dysphagia were developed. These questions dealt with various aspects of problems related to dysphagia, including assessment, management, and complications. A literature search for relevant articles was conducted using Pubmed, Embase, the Cochrane Library, and one domestic database of KoreaMed, until April 2021. The level of evidence and recommendation grade were established according to the Grading of Recommendation Assessment, Development and Evaluation methodology. Results: Early screening and assessment of videofluoroscopic swallowing were recommended for assessing the presence of dysphagia. Therapeutic methods, such as tongue and pharyngeal muscle strengthening exercises and neuromuscular electrical stimulation with swallowing therapy, were effective in improving swallowing function and quality of life in patients with dysphagia. Nutritional intervention and an oral care program were also recommended. Conclusion This guideline presents recommendations for the assessment and management of patients with oropharyngeal dysphagia, including rehabilitative strategies.

OBJECTIVES: We estimated the population prevalence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including unreported infections, through a Korea Seroprevalence Study of Monitoring of SARS-CoV-2 Antibody Retention and Transmission (K-SEROSMART) in 258 communities throughout Korea. METHODS: In August 2022, a survey was conducted among 10,000 household members aged 5 years and older, in households selected through two stage probability random sampling. During face-to-face household interviews, participants self-reported their health status, COVID-19 diagnosis and vaccination history, and general characteristics. Subsequently, participants visited a community health center or medical clinic for blood sampling. Blood samples were analyzed for the presence of antibodies to spike proteins (anti-S) and antibodies to nucleocapsid proteins (anti-N) SARS-CoV-2 proteins using an electrochemiluminescence immunoassay. To estimate the population prevalence, the PROC SURVEYMEANS statistical procedure was employed, with weighting to reflect demographic data from July 2022. RESULTS: In total, 9,945 individuals from 5,041 households were surveyed across 258 communities, representing all basic local governments in Korea. The overall population-adjusted prevalence rates of anti-S and anti-N were 97.6% and 57.1%, respectively. Since the Korea Disease Control and Prevention Agency has reported a cumulative incidence of confirmed cases of 37.8% through July 31, 2022, the proportion of unreported infections among all COVID-19 infection was suggested to be 33.9%. CONCLUSIONS The K-SEROSMART represents the first nationwide, community-based seroepidemiologic survey of COVID-19, confirming that most individuals possess antibodies to SARS-CoV-2 and that a significant number of unreported cases existed. Furthermore, this study lays the foundation for a surveillance system to continuously monitor transmission at the community level and the response to COVID-19.

Objective: As dopamine is closely linked to locomotor activities, animal studies on locomotor activities using dopaminergic agents were widely done. However, most of animal studies were performed for a short period that there is a lack of longitudinal study on the effects of dopaminergic agents on locomotor activities. This study aimed to examine the longterm effect of a dopamine D2, D3 agonist quinpirole on locomotor activities in mice using a home-cage monitoring system. Methods: The locomotor activities of Institute Cancer Research mice were measured by infrared motion detectors in home-cages under the 12-hour dark and 12-hour light condition for three days after the quinpirole injection. Quinpirole was injected at a concentration of 0.5 mg/kg intraperitoneally in the beginning of the dark phase. The locomotor activities before and after the quinpirole administration were compared by the Wilcoxon signed-rank test and one-way repeated measures ANOVA. Results: After the quinpirole administration, the 24-hour total locomotor activity did not change (p = 0.169), but activities were significantly increased in the 12-hour dark phase sum (p = 0.013) and decreased in the 12-hour light phase sum (p = 0.009). Significant increases in the activities were observed in the dark-light difference (p = 0.005) and dark-light ratio (p = 0.005) as well. Conclusion This study suggests that quinpirole injection entrains the circadian rest-activity rhythm of locomotor activities. Therefore, quinpirole can be a drug that mediates locomotor activity as a dopamine agonist as well as a modulator of the circadian rhythms.

Background: Locomotor activity in mice may have an ultradian rest-activity rhythm. However, to date, no study has shown how locomotor activity can be explained statistically using fitted cosine curves. Therefore, this study explored whether the ultradian rhythm of locomotor activity in mice could be analyzed using cosine fitting analysis. Methods: The locomotor activity of 20 male mice under a 12/12-hour dark/light cycle for 2 days was fitted to a cosine function to obtain the best fit. The mean absolute error (MAE) values were used to determine the explanatory power of the calculated cosine model for locomotor activity. The cosine fitting analysis was performed using R statistical software (version 4.1.1). Results: The mean MAE was 0.2944, whereas the mean MAE for integrating the individual analyses in the two experimental groups was 0.3284. The periods of the estimated ultradian rest-activity rhythm ranged from 1.602 to 4.168 hours. Conclusions These results suggest that locomotor activity data reflect an ultradian rhythm better than a circadian rhythm. Locomotor activity can be statistically fitted to a cosine curve under well-controlled conditions. In the future, it will be necessary to explore whether this cosine-fitting analysis can be used to analyze ultradian rhythms under different experimental conditions.

Objective: Data processing in analysis of circadian rhythm was performed in various ways. However, there was a lack of evidence for the optimal analysis of circadian rest-activity rhythm. Therefore, we aimed to perform mathematical simulations of data processing to investigate proper evidence for the optimal analysis of circadian rest-activity rhythm. Methods: Locomotor activities of 20 ICR male mice were measured by infrared motion detectors. The data of locomotor activities was processed using data summation, data average, and data moving average methods for cosinor analysis. Circadian indices were estimated according to time block, respectively. Also, statistical F and p-values were calculated by zero-amplitude test. Results: The data moving average result showed well-fitted cosine curves independent of data processing time. Meanwhile, the amplitude, MESOR, and acrophase were properly estimated within 800 seconds in data summation and data average methods. Conclusion These findings suggest that data moving average would be an optimal method for data processing in a cosinor analysis and data average within 800-second data processing time might be adaptable. The results of this study can be helpful to analyze circadian restactivity rhythms and integrate the results of the studies using different data processing methods.