As South Korea transitions into a super-aged society, addressing the challenges of aging through tailored nutritional strategies is becoming increasingly critical. This study developed a hemp seed soup designed for the elderly, using the functional benefits of hemp, a nutrient-dense ingredient rich in dietary proteins, essential fatty acids, polyphenols, and antioxidants. Soups were formulated with varying levels of hemp seed powder (0% (CS), 25% (HS25S), 50% (HS50S), and 75% (HS75S)), to address the dietary needs and physiological characteristics of the elderly, including mastication and swallowing difficulties. Increasing the hemp seed powder levels significantly enhanced the crude protein, total polyphenol, and flavonoid contents while improving the antioxidant activities, as assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays. HS75S exhibited the highest nutritional values and antioxidant activity. By contrast, food texture analysis showed that the viscosity decreased at higher hemp seed concentrations, with HS25S and HS50S demonstrating favorable textures for elderly consumption.Consumer acceptance testing identified HS50S as the most preferred sample, scoring highest in flavor, texture, and overall acceptability. This study highlights the potential of hemp seeds as a functional ingredient in developing nutritious and elderly-friendly food products. The hemp seed soup, with its enhanced protein, antioxidant properties, and tailored texture, offers a promising solution to improve the nutritional status and health of the elderly. Future studies should explore the long-term health benefits of hemp seed soup and its integration with other functional ingredients to optimize the sensory and nutritional properties.

As South Korea transitions into a super-aged society, addressing the challenges of aging through tailored nutritional strategies is becoming increasingly critical. This study developed a hemp seed soup designed for the elderly, using the functional benefits of hemp, a nutrient-dense ingredient rich in dietary proteins, essential fatty acids, polyphenols, and antioxidants. Soups were formulated with varying levels of hemp seed powder (0% (CS), 25% (HS25S), 50% (HS50S), and 75% (HS75S)), to address the dietary needs and physiological characteristics of the elderly, including mastication and swallowing difficulties. Increasing the hemp seed powder levels significantly enhanced the crude protein, total polyphenol, and flavonoid contents while improving the antioxidant activities, as assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays. HS75S exhibited the highest nutritional values and antioxidant activity. By contrast, food texture analysis showed that the viscosity decreased at higher hemp seed concentrations, with HS25S and HS50S demonstrating favorable textures for elderly consumption.Consumer acceptance testing identified HS50S as the most preferred sample, scoring highest in flavor, texture, and overall acceptability. This study highlights the potential of hemp seeds as a functional ingredient in developing nutritious and elderly-friendly food products. The hemp seed soup, with its enhanced protein, antioxidant properties, and tailored texture, offers a promising solution to improve the nutritional status and health of the elderly. Future studies should explore the long-term health benefits of hemp seed soup and its integration with other functional ingredients to optimize the sensory and nutritional properties.

As South Korea transitions into a super-aged society, addressing the challenges of aging through tailored nutritional strategies is becoming increasingly critical. This study developed a hemp seed soup designed for the elderly, using the functional benefits of hemp, a nutrient-dense ingredient rich in dietary proteins, essential fatty acids, polyphenols, and antioxidants. Soups were formulated with varying levels of hemp seed powder (0% (CS), 25% (HS25S), 50% (HS50S), and 75% (HS75S)), to address the dietary needs and physiological characteristics of the elderly, including mastication and swallowing difficulties. Increasing the hemp seed powder levels significantly enhanced the crude protein, total polyphenol, and flavonoid contents while improving the antioxidant activities, as assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays. HS75S exhibited the highest nutritional values and antioxidant activity. By contrast, food texture analysis showed that the viscosity decreased at higher hemp seed concentrations, with HS25S and HS50S demonstrating favorable textures for elderly consumption.Consumer acceptance testing identified HS50S as the most preferred sample, scoring highest in flavor, texture, and overall acceptability. This study highlights the potential of hemp seeds as a functional ingredient in developing nutritious and elderly-friendly food products. The hemp seed soup, with its enhanced protein, antioxidant properties, and tailored texture, offers a promising solution to improve the nutritional status and health of the elderly. Future studies should explore the long-term health benefits of hemp seed soup and its integration with other functional ingredients to optimize the sensory and nutritional properties.

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: 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.

Objective: Behavioral assessments that effectively predict sleep-wake states were tried in animal research. This study aimed to examine the prediction power of an infrared locomotion detector on the sleep-wake states in ICR (Institute Cancer Research) mice. We also explored the influence of the durations and ways of data processing on the prediction power. Methods: The locomotor activities of seven male mice in home cages were recorded by infrared detectors. Their sleep-wake states were assessed by video analysis. Using the receiver operating characteristic curve analysis, the cut-off score was determined, then the area under the curve (AUC) values of the infrared motion detector that predicted sleep-wake states were calculated. In order to improve the prediction power, the four ways of data processing on the prediction power were performed by Matlab 2013b. Results: In the initial analysis of raw data, the AUC value was 0.785, but it gradually reached to 0.942 after data summation. The simple data averaging and summation among four different methods showed the maximal AUC value. The 10-minute data summation improved sensitivity (0.889) and specificity (0.901) significantly from the baseline value (sensitivity 0.615; specificity 0.936) (p ＜ 0.001). Conclusion This study suggests that the locomotor activity measured by an infrared motion detector might be useful to predict the sleep-wake states in ICR mice. It also revealed that only simple data summation may improve the predictive power. Using daily locomotor activities measured by an infrared motion detector is expected to facilitate animal research related to sleep-wake states.

Objective: Behavioral assessments that effectively predict sleep-wake states were tried in animal research. This study aimed to examine the prediction power of an infrared locomotion detector on the sleep-wake states in ICR (Institute Cancer Research) mice. We also explored the influence of the durations and ways of data processing on the prediction power. Methods: The locomotor activities of seven male mice in home cages were recorded by infrared detectors. Their sleep-wake states were assessed by video analysis. Using the receiver operating characteristic curve analysis, the cut-off score was determined, then the area under the curve (AUC) values of the infrared motion detector that predicted sleep-wake states were calculated. In order to improve the prediction power, the four ways of data processing on the prediction power were performed by Matlab 2013b. Results: In the initial analysis of raw data, the AUC value was 0.785, but it gradually reached to 0.942 after data summation. The simple data averaging and summation among four different methods showed the maximal AUC value. The 10-minute data summation improved sensitivity (0.889) and specificity (0.901) significantly from the baseline value (sensitivity 0.615; specificity 0.936) (p ＜ 0.001). Conclusion This study suggests that the locomotor activity measured by an infrared motion detector might be useful to predict the sleep-wake states in ICR mice. It also revealed that only simple data summation may improve the predictive power. Using daily locomotor activities measured by an infrared motion detector is expected to facilitate animal research related to sleep-wake states.

Inflammatory myofibroblastic tumor is an uncommon tumor composed of myoblasts and various types of inflammatory infiltrates. Inflammatory myofibroblastic tumor is most common in the lungs but can be rarely found in the nasal cavity. Inflammatory myofibroblastic tumor is a rare entity that represents a diverse histologic pattern that can mimic malignant tumors. We report a case of inflammatory myofibroblastic tumor of the nasal septum in a 45-year-old man who presented with a tumor-like lesion of the nasal septum after two rounds of septoplasty.