Evaluating the validity and reliability of the Korean version of Scales for Outcomes in Parkinson's Disease-Cognition.

Objective: The Scales for Outcomes in Parkinson's Disease-Cognition (SCOPA-Cog) was developed to screen for cognition in PD. In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPA-cog. Methods: We recruited 129 PD patients from 31 clinics with movement disorders in South Korea. The original version of the SCOPA-cognition was translated into Korean using the translation-retranslation method. The test-rest method with an intraclass correlation coefficient (ICC) and Cronbach's alpha coefficient were used to assess reliability. The Spearman's Rank correlation analysis with Montreal Cognitive Assessment-Korean version (MOCA-K) and Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach's alpha coefficient was 0.797, and the ICC was 0.887. Spearman's rank correlation analysis showed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusions: Our results demonstrate that K-SCOPA-Cog exhibits good reliability and validity.

Nephrology consultation improves the clinical outcomes of patients with acute kidney injury.

Background: Acute kidney injury (AKI) is prevalent in critically ill patients and is associated with an increased risk of in-hospital mortality. Nephrology consultation may be protective, but this has rarely been evaluated in South Korea. Methods: This multicenter retrospective study was based on the electronic medical records (EMRs) of two third-affiliated hospitals. We extracted the records of patients admitted to intensive care units (ICUs) between 2011 and 2020, and retrospectively detected AKI using the modified serum creatinine criteria of the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. The AKI diagnosis date was defined as the first day of a significant change in serum creatinine level (≥0.3 mg/dL) within 48 hours. Nephrology consultation status was retrieved from the EMRs. Results: In total, 2,461 AKI patients were included; the median age was 65 years (interquartile range [IQR], 56-75 years), 1,459 (59.3%) were male, and 1,065 (43.3%) were of AKI stage 3. During a median of 5 days (IQR, 3-11 days) of ICU admission, nephrology consultations were provided to 512 patients (20.8%). Patients who received such consultations were older, had more comorbidities, and more commonly required dialysis. In a multivariable model, nephrology consultation reduced the risk of in-hospital mortality by 30% (hazard ratio, 0.71; 95% confidence interval, 0.57-0.88). Other factors significant for in-hospital mortality were older age, a higher sequential organ failure assessment (SOFA) score, sepsis, diabetes, hypertension, heart disease, and cancer. Conclusion: For AKI patients in ICUs, nephrology consultation reduced the risk of in-hospital mortality, particularly among those with multiple comorbidities. Therefore, nephrology consultation should not be omitted during ICU care.

SARS-CoV-2 Infection and Death Rates Among Maintenance Dialysis Patients During Delta and Early Omicron Waves - United States, June 30, 2021-September 27, 2022.

Persons receiving maintenance dialysis are at increased risk for SARS-CoV-2 infection and its severe outcomes, including death. However, rates of SARS-CoV-2 infection and COVID-19-related deaths in this population are not well described. Since November 2020, CDC's National Healthcare Safety Network (NHSN) has collected weekly data monitoring incidence of SARS-CoV-2 infections (defined as a positive SARS-CoV-2 test result) and COVID-19-related deaths (defined as the death of a patient who had not fully recovered from a SARS-CoV-2 infection) among maintenance dialysis patients. This analysis used NHSN dialysis facility COVID-19 data reported during June 30, 2021-September 27, 2022, to describe rates of SARS-CoV-2 infection and COVID-19-related death among maintenance dialysis patients. The overall infection rate was 30.47 per 10,000 patient-weeks (39.64 among unvaccinated patients and 27.24 among patients who had completed a primary COVID-19 vaccination series). The overall death rate was 1.74 per 10,000 patient-weeks. Implementing recommended infection control measures in dialysis facilities and ensuring patients and staff members are up to date with recommended COVID-19 vaccination is critical to limiting COVID-19-associated morbidity and mortality.

COVID-19 Surveillance After Expiration of the Public Health Emergency Declaration - United States, May 11, 2023.

On January 31, 2020, the U.S. Department of Health and Human Services (HHS) declared, under Section 319 of the Public Health Service Act, a U.S. public health emergency because of the emergence of a novel virus, SARS-CoV-2.* After 13 renewals, the public health emergency will expire on May 11, 2023. Authorizations to collect certain public health data will expire on that date as well. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority, and a number of surveillance indicators have been identified to facilitate ongoing monitoring. After expiration of the public health emergency, COVID-19-associated hospital admission levels will be the primary indicator of COVID-19 trends to help guide community and personal decisions related to risk and prevention behaviors; the percentage of COVID-19-associated deaths among all reported deaths, based on provisional death certificate data, will be the primary indicator used to monitor COVID-19 mortality. Emergency department (ED) visits with a COVID-19 diagnosis and the percentage of positive SARS-CoV-2 test results, derived from an established sentinel network, will help detect early changes in trends. National genomic surveillance will continue to be used to estimate SARS-CoV-2 variant proportions; wastewater surveillance and traveler-based genomic surveillance will also continue to be used to monitor SARS-CoV-2 variants. Disease severity and hospitalization-related outcomes are monitored via sentinel surveillance and large health care databases. Monitoring of COVID-19 vaccination coverage, vaccine effectiveness (VE), and vaccine safety will also continue. Integrated strategies for surveillance of COVID-19 and other respiratory viruses can further guide prevention efforts. COVID-19-associated hospitalizations and deaths are largely preventable through receipt of updated vaccines and timely administration of therapeutics (1-4).

Tracking COVID-19 in the United States With Surveillance of Aggregate Cases and Deaths.

Early during the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) leveraged an existing surveillance system infrastructure to monitor COVID-19 cases and deaths in the United States. Given the time needed to report individual-level (also called line-level) COVID-19 case and death data containing detailed information from individual case reports, CDC designed and implemented a new aggregate case surveillance system to inform emergency response decisions more efficiently, with timelier indicators of emerging areas of concern. We describe the processes implemented by CDC to operationalize this novel, multifaceted aggregate surveillance system for collecting COVID-19 case and death data to track the spread and impact of the SARS-CoV-2 virus at national, state, and county levels. We also review the processes established to acquire, process, and validate the aggregate number of cases and deaths due to COVID-19 in the United States at the county and jurisdiction levels during the pandemic. These processes include time-saving tools and strategies implemented to collect and validate authoritative COVID-19 case and death data from jurisdictions, such as web scraping to automate data collection and algorithms to identify and correct data anomalies. This topical review highlights the need to prepare for future emergencies, such as novel disease outbreaks, by having an event-agnostic aggregate surveillance system infrastructure in place to supplement line-level case reporting for near-real-time situational awareness and timely data.

RT-CLAD: Artificial Intelligence-Based Real-Time Chironomid Larva Detection in Drinking Water Treatment Plants.

The presence of chironomid larvae in tap water has sparked public concern regarding the water supply system in South Korea. Despite ongoing efforts to establish a safe water supply system, entirely preventing larval occurrences remains a significant challenge. Therefore, we developed a real-time chironomid larva detection system (RT-CLAD) based on deep learning technology, which was implemented in drinking water treatment plants. The acquisition of larval images was facilitated by a multi-spectral camera with a wide spectral range, enabling the capture of unique wavelet bands associated with larvae. Three state-of-the-art deep learning algorithms, namely the convolutional neural network (CNN), you only look once (YOLO), and residual neural network (ResNet), renowned for their exceptional performance in object detection tasks, were employed. Following a comparative analysis of these algorithms, the most accurate and rapid model was selected for RT-CLAD. To achieve the efficient and accurate detection of larvae, the original images were transformed into a specific wavelet format, followed by preprocessing to minimize data size. Consequently, the CNN, YOLO, and ResNet algorithms successfully detected larvae with 100% accuracy. In comparison to YOLO and ResNet, the CNN algorithm demonstrated greater efficiency because of its faster processing and simpler architecture. We anticipate that our RT-CLAD will address larva detection challenges in water treatment plants, thereby enhancing water supply security.

Improving efficiency of COVID-19 aggregate case and death surveillance data transmission for jurisdictions: current and future role of application programming interfaces (APIs).

During the coronavirus disease-2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) supplemented traditional COVID-19 case and death reporting with COVID-19 aggregate case and death surveillance (ACS) to track daily cumulative numbers. Later, as public health jurisdictions (PHJs) revised the historical COVID-19 case and death data due to data reconciliation and updates, CDC devised a manual process to update these records in the ACS dataset for improving the accuracy of COVID-19 case and death data. Automatic data transfer via an application programming interface (API), an intermediary that enables software applications to communicate, reduces the time and effort in transferring data from PHJs to CDC. However, APIs must meet specific content requirements for use by CDC. As of March 2022, CDC has integrated APIs from 3 jurisdictions for COVID-19 ACS. Expanded use of APIs may provide efficiencies for COVID-19 and other emergency response planning efforts as evidenced by this proof-of-concept. In this article, we share the utility of APIs in COVID-19 ACS.

Impact of pitavastatin on new-onset diabetes mellitus compared to atorvastatin and rosuvastatin: a distributed network analysis of 10 real-world databases.

BACKGROUND: Statin treatment increases the risk of new-onset diabetes mellitus (NODM); however, data directly comparing the risk of NODM among individual statins is limited. We compared the risk of NODM between patients using pitavastatin and atorvastatin or rosuvastatin using reliable, large-scale data. METHODS: Data of electronic health records from ten hospitals converted to the Observational Medical Outcomes Partnership Common Data Model (n = 14,605,368 patients) were used to identify new users of pitavastatin, atorvastatin, or rosuvastatin (atorvastatin + rosuvastatin) for ≥ 180 days without a previous history of diabetes or HbA1c level ≥ 5.7%. We conducted a cohort study using Cox regression analysis to examine the hazard ratio (HR) of NODM after propensity score matching (PSM) and then performed an aggregate meta-analysis of the HR. RESULTS: After 1:2 PSM, 10,238 new pitavastatin users (15,998 person-years of follow-up) and 18,605 atorvastatin + rosuvastatin users (33,477 person-years of follow-up) were pooled from 10 databases. The meta-analysis of the HRs demonstrated that pitavastatin resulted in a significantly reduced risk of NODM than atorvastatin + rosuvastatin (HR 0.72; 95% CI 0.59-0.87). In sub-analysis, pitavastatin was associated with a lower risk of NODM than atorvastatin or rosuvastatin after 1:1 PSM (HR 0.69; CI 0.54-0.88 and HR 0.74; CI 0.55-0.99, respectively). A consistently low risk of NODM in pitavastatin users was observed when compared with low-to-moderate-intensity atorvastatin + rosuvastatin users (HR 0.78; CI 0.62-0.98). CONCLUSIONS: In this retrospective, multicenter active-comparator, new-user, cohort study, pitavastatin reduced the risk of NODM compared with atorvastatin or rosuvastatin.

Whole-Exome Sequencing in Papillary Microcarcinoma: Potential Early Biomarkers of Lateral Lymph Node Metastasis.

BACKGROUND: Early identification of patients with high-risk papillary thyroid microcarcinoma (PTMC) that is likely to progress has become a critical challenge. We aimed to identify somatic mutations associated with lateral neck lymph node (LN) metastasis (N1b) in patients with PTMC. METHODS: Whole-exome sequencing (WES) of 14 PTMCs with no LN metastasis (N0) and 13 N1b PTMCs was performed using primary tumors and matched normal thyroid tissues. RESULTS: The mutational burden was comparable in N0 and N1b tumors, as the median number of mutations was 23 (range, 12 to 46) in N0 and 24 (range, 12 to 50) in N1b PTMC (P=0.918). The most frequent mutations were detected in PGS1, SLC4A8, DAAM2, and HELZ in N1b PTMCs alone, and the K158Q mutation in PGS1 (four patients, Fisher's exact test P=0.041) was significantly enriched in N1b PTMCs. Based on pathway analysis, somatic mutations belonging to the receptor tyrosine kinase-RAS and NOTCH pathways were most frequently affected in N1b PTMCs. We identified four mutations that are predicted to be pathogenic in four genes based on Clinvar and Combined Annotation-Dependent Depletion score: BRAF, USH2A, CFTR, and PHIP. A missense mutation in CFTR and a nonsense mutation in PHIP were detected in N1b PTMCs only, although in one case each. BRAF mutation was detected in both N0 and N1b PTMCs. CONCLUSION: This first comprehensive WES analysis of the mutational landscape of N0 and N1b PTMCs identified pathogenic genes that affect biological functions associated with the aggressive phenotype of PTMC.

Decreases in COVID-19 Cases, Emergency Department Visits, Hospital Admissions, and Deaths Among Older Adults Following the Introduction of COVID-19 Vaccine - United States, September 6, 2020-May 1, 2021.

Throughout the COVID-19 pandemic, older U.S. adults have been at increased risk for severe COVID-19-associated illness and death (1). On December 14, 2020, the United States began a nationwide vaccination campaign after the Food and Drug Administration's Emergency Use Authorization of Pfizer-BioNTech COVID-19 vaccine. The Advisory Committee on Immunization Practices (ACIP) recommended prioritizing health care personnel and residents of long-term care facilities, followed by essential workers and persons at risk for severe illness, including adults aged ≥65 years, in the early phases of the vaccination program (2). By May 1, 2021, 82%, 63%, and 42% of persons aged ≥65, 50-64, and 18-49 years, respectively, had received ≥1 COVID-19 vaccine dose. CDC calculated the rates of COVID-19 cases, emergency department (ED) visits, hospital admissions, and deaths by age group during November 29-December 12, 2020 (prevaccine) and April 18-May 1, 2021. The rate ratios comparing the oldest age groups (≥70 years for hospital admissions; ≥65 years for other measures) with adults aged 18-49 years were 40%, 59%, 65%, and 66% lower, respectively, in the latter period. These differential declines are likely due, in part, to higher COVID-19 vaccination coverage among older adults, highlighting the potential benefits of rapidly increasing vaccination coverage.

Gene expression profiles of diabetic kidney disease and neuropathy in eNOS knockout mice: Predictors of pathology and RAS blockade effects.

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are two common diabetic complications. However, their pathogenesis remains elusive and current therapies are only modestly effective. We evaluated genome-wide expression to identify pathways involved in DKD and DPN progression in db/db eNOS-/- mice receiving renin-angiotensin-aldosterone system (RAS)-blocking drugs to mimic the current standard of care for DKD patients. Diabetes and eNOS deletion worsened DKD, which improved with RAS treatment. Diabetes also induced DPN, which was not affected by eNOS deletion or RAS blockade. Given the multiple factors affecting DKD and the graded differences in disease severity across mouse groups, an automatic data analysis method, SOM, or self-organizing map was used to elucidate glomerular transcriptional changes associated with DKD, whereas pairwise bioinformatic analysis was used for DPN. These analyses revealed that enhanced gene expression in several pro-inflammatory networks and reduced expression of development genes correlated with worsening DKD. Although RAS treatment ameliorated the nephropathy phenotype, it did not alter the more abnormal gene expression changes in kidney. Moreover, RAS exacerbated expression of genes related to inflammation and oxidant generation in peripheral nerves. The graded increase in inflammatory gene expression and decrease in development gene expression with DKD progression underline the potentially important role of these pathways in DKD pathogenesis. Since RAS blockers worsened this gene expression pattern in both DKD and DPN, it may partly explain the inadequate therapeutic efficacy of such blockers.

RNA-Seq identifies condition-specific biological signatures of ischemia-reperfusion injury in the human kidney.

BACKGROUND: Acute kidney injury (AKI) is defined as a sudden event of kidney failure or kidney damage within a short period. Ischemia-reperfusion injury (IRI) is a critical factor associated with severe AKI and end-stage kidney disease (ESKD). However, the biological mechanisms underlying ischemia and reperfusion are incompletely understood, owing to the complexity of these pathophysiological processes. We aimed to investigate the key biological pathways individually affected by ischemia and reperfusion at the transcriptome level. RESULTS: We analyzed the steady-state gene expression pattern of human kidney tissues from normal (pre-ischemia), ischemia, and reperfusion conditions using RNA-sequencing. Conventional differential expression and self-organizing map (SOM) clustering analyses followed by pathway analysis were performed. Differential expression analysis revealed the metabolic pathways dysregulated in ischemia. Cellular assembly, development and migration, and immune response-related pathways were dysregulated in reperfusion. SOM clustering analysis highlighted the ischemia-mediated significant dysregulation in metabolism, apoptosis, and fibrosis-related pathways, while cell growth, migration, and immune response-related pathways were highly dysregulated by reperfusion after ischemia. The expression of pro-apoptotic genes and death receptors was downregulated during ischemia, indicating the existence of a protective mechanism against ischemic injury. Reperfusion induced alterations in the expression of the genes associated with immune response such as inflammasome and antigen representing genes. Further, the genes related to cell growth and migration, such as AKT, KRAS, and those related to Rho signaling, were downregulated, suggestive of injury responses during reperfusion. Semaphorin 4D and plexin B1 levels were also downregulated. CONCLUSIONS: We show that specific biological pathways were distinctively involved in ischemia and reperfusion during IRI, indicating that condition-specific therapeutic strategies may be imperative to prevent severe kidney damage after IRI in the clinical setting.

Semaphorin-3C Is Upregulated in Polycystic Kidney Epithelial Cells and Inhibits Angiogenesis of Glomerular Endothelial Cells.

BACKGROUND: Polycystic kidney disease (PKD) is a hereditary disease characterized by cyst formation in the kidneys bilaterally. It has been observed that semaphorin-3C (SEMA3C) is overexpressed in polycystic kidney epithelial cells. It is hypothesized that upregulated SEMA3C would contribute to survival of polycystic kidney epithelial cells. Furthermore, as the kidney is a highly vascularized organ, the secreted SEMA3C from PKD epithelial cells will affect glomerular endothelial cells (GECs) in a paracrine manner. METHODS: To evaluate the effect of SEMA3C on renal cells, siSEMA3C-treated PKD epithelial cells were used for further analysis, and GECs were exposed to recombinant SEMA3C (rSEMA3C). Also, co-culture and treatment of conditioned media were employed to confirm whether PKD epithelial cells could influence on GECs via SEMA3C secretion. RESULTS: SEMA3C knockdown reduced proliferation of PKD epithelial cells. In case of GECs, exposure to rSEMA3C decreased angiogenesis, which resulted from suppressed migratory ability not cell proliferation. CONCLUSIONS: This study indicates that SEMA3C is the aggravating factor in PKD. Thus, it is proposed that targeting SEMA3C can be effective to mitigate PKD.

Transcriptional networks of progressive diabetic peripheral neuropathy in the db/db mouse model of type 2 diabetes: An inflammatory story.

Diabetic peripheral neuropathy is the most common complication of diabetes and a source of considerable morbidity. Numerous molecular pathways are linked to neuropathic progression, but it is unclear whether these pathways are altered throughout the course of disease. Moreover, the methods by which these molecular pathways are analyzed can produce significantly different results; as such it is often unclear whether previously published pathways are viable targets for novel therapeutic approaches. In the current study we examine changes in gene expression patterns in the sciatic nerve (SCN) and dorsal root ganglia (DRG) of db/db diabetic mice at 8, 16, and 24 weeks of age using microarray analysis. Following the collection and verification of gene expression data, we utilized both self-organizing map (SOM) analysis and differentially expressed gene (DEG) analysis to detect pathways that were altered at all time points. Though there was some variability between SOM and DEG analyses, we consistently detected altered immune pathways in both the SCN and DRG over the course of disease. To support these results, we further used multiplex analysis to assess protein changes in the SCN of diabetic mice; we found that multiple immune molecules were upregulated at both early and later stages of disease. In particular, we found that matrix metalloproteinase-12 was highly upregulated in microarray and multiplex data sets suggesting it may play a role in disease progression.

Meningitis outbreak investigation in Nkoranza South Municipality in Brong Ahafo Region, Ghana, February, 2016.

The occurrence of communicable diseases highlights the need to have well-trained field epidemiologists at the forefront in the fight against these diseases, especially during an outbreak. Training for outbreak investigation is most effective when participants can develop their competencies in a practical exercise. This is a simulation of the steps in meningitis outbreak investigation conducted in Ghana in February 2016 by Ghana Field Epidemiology Training Programme (FELTP) residents and the public health technical team of the Nkoranza South Municipality as a field epidemiologist. This case study is suited to reinforce principles and skills already covered in a lecture or in background reading by providing a practical training beyond the scope of theoretical learning. It is primarily intended for training novice public health practitioners who should be able to complete the exercises in 3 hours.

Outbreak investigation of measles in Kaduna State, Northwestern Nigeria, 2015.

Sub-Saharan Africa reports repeated outbreaks of measles, a vaccine preventable disease, which is notifiable under the Integrated Disease Surveillance and Response strategy in Nigeria. Nigeria has reported several outbreaks of measles in the last three years. Poor immunization coverage and weak health systems have been related with measles. This case study is based on real events that occurred during the 2015 outbreak of measles in Kaduna state Northwestern Nigeria. This case study was based upon real events that occurred in community X in Igabi LGA of Kaduna state. However, some of the results were edited to allow the case study to be completed in a facilitated classroom session. Knowledge and practice of investigating outbreaks is a key public health function of public health workers. The purpose of this case study is to simulate outbreak investigation for teaching of postgraduate public health practitioners. The participants should have received lectures or other training on outbreak investigation without the practical experience of investigating an outbreak but are being prepared to investigate outbreaks in the field. This case study should be taken in a classroom setting and should take two hours to complete.

From known to the unknown: investigating an unusual outbreak of viral exanthema in a secondary school in Abeokuta, Nigeria, 2015.

Investigating an outbreak of disease requires mastery of a set of skills and collaboration among different cadres of health workers. Although you want to focus on a specific disease, you need to keep your mind open to possibilities. This case study is based on investigation of an outbreak of rashes suspected to be measles but which proved to be otherwise. It reinforces the knowledge of the steps in outbreak investigation which should have been covered in classroom lecture or background reading. This case study is best suited for basic level of training in field epidemiology and can be completed within 2-3 hours.

Investigating a cholera outbreak in Kaiso Fishing Village, Hoima District, Uganda, October 2015.

Globally, even though improvements have been made to effective surveillance and response, communicable diseases such as cholera remain high priorities for national health programs, especially in Africa. High-quality surveillance information coupled with adequate laboratory facilities are effective in curbing outbreaks from such diseases, ultimately reducing morbidity and mortality. One way of building this capacity is through simulation of response to such health events. This case study based on a cholera outbreak investigated by FETP trainees in October 2015 in Uganda can be used to reinforce skills of frontline FETP trainees and other novice public health practitioners through a practical simulation approach. This activity should be completed in 2.5 hours.

Outbreak of pertussis at community A in Dormaa Municipality, Ghana, August 2016.

Pertussis is a vaccine preventable disease (VPD) monitored by the World Health Organization (WHO). Despite a long-established Pertussis immunization system, the re-emergence of the disease in some countries stressed the need to have well-trained field epidemiologists at the forefront in the fight against these VPDs, especially during an outbreak. Practical, hands-on training is useful for clearer understanding of the principles and development of competencies relevant to outbreak investigation, which will enhance field practice; case method training using realistic public health scenarios helps trainees put into practice learned theory. As such, this case study was adopted from a real Pertussis outbreak investigation that was conducted by Ghana's Field Epidemiology Training Program residents, together with the rapid response team members of Dormaa Municipal health directorate in August 2016. It was primarily designed for training novice public health practitioners in a facilitated classroom setting. Participants should be able to complete the exercises in approximately 3 hours.

Sleepiness and Depression in Parkinson's Disease Patients Treated with Ropinirole and Levodopa.

OBJECTIVE: We aimed to investigate the effect of ropinirole on excessive daytime sleepiness (EDS) and depression in Parkinson's disease (PD) with a large population. METHODS: We conducted a cross-sectional observational study at nine hospitals in Korea between April 24, 2013, and April 22, 2015. We analyzed the demographic and clinical features, other medical history, history of antiparkinsonian medication within 6 months, Hoehn and Yahr stage (HY stage), Unified Parkinson's Disease Rating Scale (UPDRS) part II and III, Epworth Sleepiness Scale (ESS), and 30-item Geriatric Depression Scale (GDS-30). RESULTS: Four-hundred-thirteen patients with PD (mean age: 65.2 ± 9.0 years; men: 227 patients) were analyzed. Multivariate logistic regression analysis showed that age at examination, UPDRS II, and GDS-30 were independent risk factors for EDS and that sex, UPDRS II, and ESS were independent risk factors for depression. CONCLUSION: Our large group study did not find any significant associations of ropinirole with EDS and depression in Korean PD patients.