Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Background: and Purpose Parkinson’s disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information. Methods: We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods. Results: During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931–0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922. Conclusions A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

Background: and Purpose Parkinson’s disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information. Methods: We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods. Results: During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931–0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922. Conclusions A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Background: and Purpose Parkinson’s disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information. Methods: We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods. Results: During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931–0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922. Conclusions A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by alveolar destruction and increased inflammation, leading to respiratory symptoms. This study aimed to identify the traits for COPD progression from mild to severe stages. Additionally, we explored the correlation between coronavirus disease-2019 (COVID-19) and COPD to uncover overlapping respiratory patterns. Materials and Methods: Bulk RNA sequencing was conducted on data from 43 healthy individuals and 39 COPD patients across one dataset (GSE239897) to distinguish COPD characteristics. Single-cell RNA analysis was then performed on samples from seven mild patients, seven moderate patients, and three severe patients from three datasets (GSE167295, GSE173896, and GSE227691) to analyze disease progression. Finally, single-nuclei RNA analysis was applied to data from seven healthy individuals and 20 COVID-19 patients from one dataset (GSE171524) to compare the two conditions. Results: Bulk RNA sequencing revealed enhanced inflammatory pathways in COPD patients, indicating increased inflammation.Single-cell RNA sequencing showed a stronger inflammatory response from mild to moderate COPD with a decrease from moderate to severe stages. COVID-19 displayed similar biological patterns to moderate COPD, suggesting that stage-specific COPD analysis could enhance COVID-19 management. Conclusion The analysis found that immune responses increased from mild to moderate stages but declined in severe cases, marked by reduced pulmonary T cell activation. The overlap between moderate COPD and COVID-19 suggests shared therapeutic strategies, warranting further investigation.

Hematuria is a relatively common condition among school-aged children. Because international guidelines for asymptomatic hematuria in children are unavailable, developing practical guidelines for the diagnosis and management of asymptomatic hematuria based on scientific evidence while considering real-world practice settings, values, and patient and physician preferences is essential. The Korean Society of Pediatric Nephrology developed clinical guidelines to address key questions regarding the diagnosis and management of asymptomatic hematuria in children.

Hematuria is a relatively common condition among school-aged children. Because international guidelines for asymptomatic hematuria in children are unavailable, developing practical guidelines for the diagnosis and management of asymptomatic hematuria based on scientific evidence while considering real-world practice settings, values, and patient and physician preferences is essential. The Korean Society of Pediatric Nephrology developed clinical guidelines to address key questions regarding the diagnosis and management of asymptomatic hematuria in children.