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 This study evaluated the diagnostic utility of an anti-signal-recognition particle 54 (anti-SRP54) antibody-based enzyme-linked immunosorbent assay (ELISA) as well as the clinical, serological, and pathological characteristics of patients with SRP immune-mediated necrotizing myopathy (IMNM). Methods: We evaluated 87 patients with idiopathic inflammatory myopathy and 107 healthy participants between January 2002 and December 2023. The sensitivity and specificity of the ELISA for anti-SRP54 antibodies were assessed, and the clinical profiles of patients with antiSRP54 antibodies were determined. Results: The ELISA for anti-SRP54 antibodies had a sensitivity and specificity of 88% and 99%, respectively, along with a test–retest reliability of 0.92 (p<0.001). The 32 patients diagnosed with anti-SRP IMNM using a line-blot immunoassay included 28 (88%) who tested positive for anti-SRP54 antibodies using the ELISA, comprising 12 (43%) males and 16 (57%) females whose median ages at symptom onset and diagnosis were 43.0 years and 43.5 years, respectively. Symptoms included proximal muscle weakness in all 28 (100%) patients, neck weakness in 9 (32%), myalgia in 15 (54%), dysphagia in 5 (18%), dyspnea in 4 (14%), dysarthria in 2 (7%), interstitial lung disease in 2 (7%), and myocarditis in 2 (7%). The median serum creatine kinase (CK) level was 7,261 U/L (interquartile range: 5,086–10,007 U/L), and the median anti-SRP54 antibody level was 2.0 U/mL (interquartile range: 1.0–5.6 U/mL). The serum CK level was significantly higher in patients with coexisting anti-Ro-52 antibodies. Conclusions This study has confirmed the reliability of the ELISA for anti-SRP54 antibodies and provided insights into the clinical, serological, and pathological characteristics of South Korean patients with anti-SRP IMNM.

Background: and Purpose Parkinson’s disease (PD) significantly impacts the quality of life via both motor and nonmotor symptoms. Exercise is a valuable nonpharmacological intervention that can alleviate PD symptoms and slow disease progression. Understanding the factors that motivate and restrict exercise in PD patients is essential for promoting engagement.This study aimed to identify the motivators and barriers affecting exercise in PD patients. Methods: This cross-sectional study assessed exercise habits, motivators, and barriers among PD patients with a modified Hoehn and Yahr stage of ≤2.5. Participants were categorized into non-, low-, and high-exercise groups based on the World Health Organization guidelines. The International Physical Activity Questionnaire, the Korean version of the Sport Motivation Scale, and a barriers-to-exercise questionnaire were utilized. Results: Data from 165 of 196 enrolled patients were analyzed: 28 (17.0%), 88 (53.3%), and 49 (29.7%) in the non-, low-, and high-exercise groups, respectively. The nonexercise group demonstrated higher levels of fatigue and apathy, and more-severe cardiovascular, mood, intellectual, attention, gastrointestinal, and urinary symptoms. While all groups recognized the benefit of exercise, those in the nonexercise group viewed PD symptoms and depressive mood as major barriers, whereas those in the high-exercise group were primarily motivated by personal satisfaction. Conclusions This study highlights the importance of enjoyment and personal satisfaction to the maintenance of exercise habits among PD patients. By enhancing specific motivators and overcoming barriers, particularly PD symptoms and related nonmotor symptoms, tailored interventions can be implemented to increase exercise adherence and, eventually, improve the quality of life of PD patients.