Background: This study aimed to develop standard job categories for constructing a job-exposure matrix (JEM) for police officers in South Korea and to evaluate their applicability. Methods: We examined standard job codes related to police personnel management and compared them with job classifications from police publications. Using R Shiny, we developed a web-based search tool for standard codes. A pilot survey of 130 police officers assessed the codes' applicability and relevance to health-related hazardous factors. Results: Eighty-seven standard functional codes used in the police personnel management system POOL were organized into minor categories as the basic units of standard jobs. These were grouped into 20 sub-major categories and further consolidated into 10 major categories to develop the standard job codes. The responses to the standard job codes in the pilot survey were 75% accurate compared with the final expert evaluation results and 99.2% accurate compared with the algorithm-based automatic allocation results. The results of the job-hazardous factor network analysis revealed that the most frequently reported hazardous factor was emotional labor, followed by night shifts and electromagnetic waves. Emotional labor was identified as the top hazardous factor in six out of the nine standard job categories. Conclusions The standard job codes developed in this study were designed in connection with the personnel management system for police officers, making them well-suited for constructing a comprehensive JEM for the entire police force.

Objective: The Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson’s disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels. Methods: In this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning methods and different cutoff criteria were conducted on an additional 91 consecutive patients with PD. Results: The cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60–80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10–12 years, and 21 or 20 years for 7–9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250). Conclusion Both the age- and education-adjusted cutoff methods and machine learning methods demonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.

Background: To precisely build a healthcare delivery system at regional levels, local patients’ healthcare service utilization patterns must be examined. Hence, this study utilized trend analysis of the relevance index of each disease of each essential medical service field at the municipal and provincial levels. Methods: This study analyzed customized databases released by the National Health Insurance Service from 2016–2020. Diseases defined in the Korean National Burden of Disease (KNBD) study were categorized into the following essential medical service fields:trauma care, cardiocerebrovascular, maternal and neonatal, mental health, infection, cancer, older adults’ care and rehabilitation, and others. Relevance index, the percentage of medical service utilization in a region by the residents of that region relative to their total medical service utilization, was examined by region (17 municipal and provincial regions) and disease area. The relevance index was determined based on the number of patients and the total outof-pocket expenses.Result: Eight of the 17 regions showed over a 90.0% relevance index in the infection area. In the cancer area, 14 regions (not including Seoul, Daegu, and Busan) had a relevance index lower than 75.0%. Throughout the analysis period (2016–2020), there were no significant variations in the relevance index. Diseases such as bone and connective tissue cancer (39.0%), neural tube defects (16.7%), and autism (57.1%) had low relevance index in the essential medical service fields. In all 17 regions, the relevance index of inpatients was lower than that of outpatients, and that for out-of-pocket expenses was lower than that based on the number of patients. Conclusion The relevance index of major diseases of each essential medical service field calculated in this study can provide good indicators for monitoring the level of an independent regional healthcare delivery system.

Objective: The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson’s disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI. Methods: In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson’s Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method. Results: Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87–0.89). Conclusion Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.

Purpose: Hereditary parkinsonism genes consist of causative genes of familial Parkinson’s disease (PD) with a locus symbol prefix (PARK genes) and hereditary atypical parkinsonian disorders that present atypical features and limited responsiveness to levodopa (non-PARK genes). Although studies have shown that hereditary parkinsonism genes are related to idiopathic PD at the phenotypic, gene expression, and genomic levels, no study has systematically investigated connectivity among the proteins encoded by these genes at the protein-protein interaction (PPI) level. Materials and Methods: Topological measurements and physical interaction enrichment were performed to assess PPI networks constructed using some or all the proteins encoded by hereditary parkinsonism genes (n=96), which were curated using the Online Mendelian Inheritance in Man database and literature. Results: Non-PARK and PARK genes were involved in common functional modules related to autophagy, mitochondrial or lysosomal organization, catecholamine metabolic process, chemical synapse transmission, response to oxidative stress, neuronal apoptosis, regulation of cellular protein catabolic process, and vesicle-mediated transport in synapse. The hereditary parkinsonism proteins formed a single large network comprising 51 nodes, 83 edges, and three PPI pairs. The probability of degree distribution followed a power-law scaling behavior, with a degree exponent of 1.24 and a correlation coefficient of 0.92. LRRK2 was identified as a hub gene with the highest degree of betweenness centrality; its physical interaction enrichment score was 1.28, which was highly significant. Conclusion Both PARK and non-PARK genes show high connectivity at the PPI and biological functional levels.

Purpose: This study aimed to determine the effect of an exercise intervention on subjective cancer-related fatigue (CRF) and pro-inflammatory cytokine levels in breast cancer survivors (BCS). Methods: BCS with greater than moderate CRF (≥ 4) were recruited and randomly assigned to experimental or control groups. The experimental group participated in a 12-week exercise adherence program (Better Life after Cancer - Energy, Strength, and Support; BLESS). Interleukin (IL)-6 and tumor necrosis factor-α (TNF-α) levels were determined at 3 time points (M1: baseline, M2: post-intervention, and M4: 6 months after intervention). Subjective fatigue was measured using the Korean version of the revised Piper Fatigue Scale. Results: In this analysis of participants with physiological fatigue measures available (19 experimental, 21 control), there were no statistically significant differences in IL-6 (F = 1.157, p = 0.341), TNF-α levels (F = 0.878, p = 0.436), and level of fatigue (F = 2.067, p = 0.118) between the 2 groups at baseline. Fatigue in the experimental group showed statistically significant improvement compared to the control only at M2 (p = 0.022). There was no significant relationship between subjective and physiological fatigue at the 3 measurement points. Conclusion The BLESS intervention improved CRF in BCS immediately at post-intervention, and this study presents clinical feasibility for the management of CRF in BCS in the early survivorship phase who are already experiencing fatigue.

Background: The present study aimed to update the methodology to estimate cause-specific disability weight (DW) for the calculation of disability adjusted life year (DALY) and health-adjusted life expectancy (HALE) based on the opinion of medical professional experts. Furthermore, the study also aimed to compare and assess the size of DW according to two analytical methods and estimate the most valid DW from the perspective of years lost due to disability and HALE estimation. Methods: A self-administered web-based survey was conducted ranking five causes of disease. A total of 901 participants started the survey and response data of 806 participants were used in the analyses. In the process of rescaling predicted probability to DW on a scale from 0 to 1, two models were used for two groups: Group 1 (physicians and medical students) and Group 2 (nurses and oriental medical doctors). In Model 1, predicted probabilities were rescaled according to the normal distribution of DWs. In Model 2, the natural logarithms of predicted probabilities were rescaled according to the asymmetric distribution of DWs. Results: We estimated DWs for a total of 313 causes of disease in each model and group. The mean of DWs according to the models in each group was 0.490 (Model 1 in Group 1), 0.378 (Model 2 in Group 1), 0.506 (Model 1 in Group 2), and 0.459 (Model 2 in Group 2), respectively. About two-thirds of the causes of disease had DWs of 0.2 to 0.4 in Model 2 in Group 1. In Group 2, but not in Group 1, there were some cases where the DWs had a reversed order of severity. Conclusion We attempted to calculate DWs of 313 causes of disease based on the opinions of various types of medical professionals using the previous analysis methods as well as the revised analysis method. The DWs from this study can be used to accurately estimate DALY and health life expectancy, such as HALE, in the Korean population.

This study aimed at constructing a draft genome of the adult female worm Toxocara canis using next-generation sequencing (NGS) and de novo assembly, as well as to find new genes after annotation using functional genomics tools. Using an NGS machine, we produced DNA read data of T. canis. The de novo assembly of the read data was performed using SOAPdenovo. RNA read data were assembled using Trinity. Structural annotation, homology search, functional annotation, classification of protein domains, and KEGG pathway analysis were carried out. Besides them, recently developed tools such as MAKER, PASA, Evidence Modeler, and Blast2GO were used. The scaffold DNA was obtained, the N50 was 108,950 bp, and the overall length was 341,776,187 bp. The N50 of the transcriptome was 940 bp, and its length was 53,046,952 bp. The GC content of the entire genome was 39.3%. The total number of genes was 20,178, and the total number of protein sequences was 22,358. Of the 22,358 protein sequences, 4,992 were newly observed in T. canis. Following proteins previously unknown were found: E3 ubiquitin-protein ligase cbl-b and antigen T-cell receptor, zeta chain for T-cell and B-cell regulation; endoprotease bli-4 for cuticle metabolism; mucin 12Ea and polymorphic mucin variant C6/1/40r2.1 for mucin production; tropomodulin-family protein and ryanodine receptor calcium release channels for muscle movement. We were able to find new hypothetical polypeptides sequences unique to T. canis, and the findings of this study are capable of serving as a basis for extending our biological understanding of T. canis.
Adult ; B-Lymphocytes ; Base Composition ; Classification ; DNA ; Female ; Genome* ; Genomics ; Humans ; Larva Migrans, Visceral* ; Metabolism ; Mucins ; Peptides ; Protein Structure, Tertiary ; Receptors, Antigen, T-Cell ; RNA ; Ryanodine Receptor Calcium Release Channel ; T-Lymphocytes ; Toxocara canis* ; Toxocara* ; Transcriptome ; Ubiquitin-Protein Ligases