Background: An increased incidence of hair loss disorders has been noted among patients with coronavirus disease 2019 (COVID-19) and individuals vaccinated against COVID-19. However, research involving large populations on this topic is lacking. Objective: To investigate the risks associated with developing hair loss disorders in patients with COVID-19 and individuals vaccinated against COVID-19. Methods: This nationwide, population-based, cross-sectional study included patients diagnosed with COVID-19 and healthy individuals without a history of COVID-19 infection registered in the Korean National Health Insurance Service (NHIS) database between January 1, 2021, and December 31, 2021. COVID-19 infection and vaccine databases were integrated using this NHIS database. The odds ratios of hair loss disorders were compared using multivariate logistic regression models. Results: COVID-19 infection was associated with an increased risk of total alopecia (adjusted odds ratio [aOR], 1.076; 95% confidence interval [CI], 1.002–1.156), although this association was not significant after propensity score matching. No significant associations were found between COVID-19 infection and alopecia areata or telogen effluvium. However, COVID-19 vaccination was positively correlated with total alopecia (aOR, 1.266; 95% CI, 1.191–1.346), alopecia areata (aOR, 1.243; 95% CI, 1.154–1.339), and telogen effluvium (aOR, 1.495; 95% CI, 1.133–1.974). Conclusion COVID-19 vaccination was positively correlated with hair loss disorders but not COVID-19 infection. However, given the advantages of vaccines in reducing COVID-19 mortality and morbidity, alopecia may be relatively reversible and less severe. Physicians need to understand the benefits and possible side effects of the COVID-19 vaccine.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Purpose: In the present study, we determined the prevalence of obstructive meibomian gland dysfunction (MGD), hyposecretory MGD, grossly normal MG, and hypersecretory MGD in patients with dry eye syndrome using lipid layer thickness (LLT) and MG dropout. Methods: Eighty-eight patients with dry eye syndrome were included in the study. Patients were categorized into four groups according to the LLT and weighted total meiboscore. The proportion of patients in each group was calculated. The age, sex, Ocular Surface Disease Index, LLT, Schirmer, tear film breakup time, cornea stain, weighted total meiboscore, expressibility, and quality of meibum were compared between the four groups. Results: Fifteen eyes (17.0%) had obstructive MGD, two eyes (2.3%) had hyposecretory MGD, 40 eyes (45.5%) had grossly normal MG, and 17 eyes (19.3%) had hypersecretory MGD. The obstructive MGD group was younger than the grossly normal MG group. In obstructive MGD, the ratio of men to women was higher than that of the other groups. However, Ocular Surface Disease Index, Schirmer, tear film breakup time, and corneal stain did not show statistically significant differences between the four groups. The meibum expressibility of the hyposecretoy MGD group was worse than those of the other groups. The meibum expressibility of the hyposecretoy MGD group was poor than those of the obstructive and hypersecretory MGD group. Conclusions This categorization was expected to help determine the best treatment method for dry eye syndrome, according to the MG status.

The hepatitis A virus (HAV) induces severe acute liver injury and is adapted to human and monkey cell lines but not other cells. In this study, the HAV was inoculated into porcine kidney (PK-15) cells to determine its infectivity in porcine cells. The growth pattern of the HAV in PK-15 cells was compared with its growth pattern in fetal rhesus kidney (FRhK-4) cells. The growth of HAV was less efficient in PK-15 cells. In conclusion, HAV replication was verified in PK-15 cells for the first time. Further investigations will be needed to identify the HAV-restrictive mechanisms in PK-15 cells.

Background: Self-leadership, an action strategy that can maximize individual capabilities, can affect the organizational commitment of dental hygienists and ultimately improve the quality of medical services. This study aims to demonstrate the need for self-leadership and organizational commitment for dental hygienists and develop measures to improve the quality of medical services. Methods: An online survey of dental hygienists working at dental hospitals and clinics in Seoul and Gyeonggi province, Republic of Korea was conducted from March 28 to May 1, 2022. A total of 341 questionnaires were returned and analyzed. The measurement tools were modified and supplemented based on the theories and models developed by Manz for self-leadership, Mowday for organizational commitment, and Cronin and Taylor for medical services. Descriptive statistics, independent t-tests, ANOVA, simple regression, and multiple regression analyses were performed using SPSS 25.0. Results: In leadership education, self-leadership is based on participation experience, the number of participants, and when and where it is received. Organizational commitment comes from participation experience, and the quality of medical services has been found to affect participation experience and location. Self-leadership had an effect on the quality of medical services (β=0.497, t=10.551, p＜0.001; β =0.599, t=13.783, p＜0.001; β=0.353, t=7.601, p＜0.001) and organizational commitment was found to have a mediating effect. Conclusion Dental hygienists’ self-leadership has a positive effect on the quality of medical services through the formation of appropriate interrelationships within the organization. Therefore, self-leadership programs should be developed, participated in, and promoted to improve the self-leadership of dental hygienists. Moreover, hospitals should improve their environment to provide and improve self-leadership education.

Purpose: Identification of type I protein arginine methyltransferase (PRMT) substrates and their functional significance during tumorigenesis is becoming more important. The present study aimed to identify target substrates for type I PRMT using 2-dimensional (2D) gel electrophoresis (GE) and 2D Western blotting (WB). Methods: Using immunoblot analysis, we compared the expression of type I PRMTs and endogenous levels of arginine methylation between the primary colorectal cancer (CRC) and adjacent noncancerous tissues paired from the same patient. To identify arginine-methylated proteins in HCT116 cells, we carried out 2D-GE and 2D-WB with a type I PRMT product-specific antibody (anti-dimethyl-arginine antibody, asymmetric [ASYM24]). Arginine-methylated protein spots were identified by mass spectrometry, and messenger RNA (mRNA) levels corresponding to the identified proteins were analyzed using National Center for Biotechnology Information (NCBI) microarray datasets between the primary CRC and noncancerous tissues. Results: Type I PRMTs and methylarginine-containing proteins were highly maintained in CRC tissues compared to noncancerous tissues. We matched 142 spots using spot analysis software between a Coomassie blue (CBB)-stained 2D gel and 2D-WB, and we successfully identified 7 proteins that reacted with the ASYM24 antibody: CACYBP, GLOD4, MAPRE1, CCT7, TKT, CK8, and HSPA8. Among these proteins, the levels of 4 mRNAs including MAPRE1, CCT7, TKT, and HSPA8 in CRC tissues showed a statistically significant increase compared to noncancerous tissues from patients using the NCBI microarray datasets. Conclusion Our results indicate that the method shown here is useful in identifying arginine-methylated proteins, and significance of arginine modification in the proteins identified here should be further identified during CRC development.