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The objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms. We developed machine learning models to predict the FAZ area from OCT B-scan images of eyes without retinal vascular diseases. The study involved three models: Model 1 predicted the FAZ length from B-scan images; Model 2 estimated the FAZ area from the predicted length using 1, 3, or 5 horizontal measurements; and Model 3 converted the FAZ area from pixels to mm2. The models' performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R2). The FAZ area was subsequently estimated by sequentially applying Models 1→2→3 on a new dataset. Model 1 achieved a MAE of 2.86, MSE of 17.56, and R2 of 0.87. Model 2's performance improved with the number of horizontal measurements, with the best results obtained using 5 lines (MAE: 40.36, MSE: 3129.65, R2: 0.95). Model 3 achieved a MAE of 1.52e-3, MSE of 4.0e-6, and R2 of 1.0. The accuracy of FAZ area estimation increased with the number of B-scan images used, with the correlation coefficient rising from 0.475 (1 line) to 0.596 (5 lines). Bland-Altman analysis showed improved agreement between predicted and actual FAZ areas with increasing B-scan images, evidenced by decreasing biases and narrower limits of agreement. This study successfully developed machine learning models capable of predicting FAZ area from OCT B-scan images. These findings demonstrate the potential for using OCT images to predict OCTA data, particularly in populations where OCTA imaging is challenging, such as children and the elderly. Future studies could explore the developmental mechanisms of the FAZ and macula, providing new insights into retinal health across different age groups.

Objectives Translation of the Professional Fulfillment Index (PFI) into Japanese would be more useful than the currently developed scales for appropriately measuring burnout and professional fulfillment in healthcare professionals. This study aimed to develop the Japanese version of the PFI and examine its internal consistency, structural validity, and convergent validity in healthcare professionals. Methods Healthcare professionals in Japan were recruited online. The survey was conducted from October to November 2022. Internal consistency was tested using Cronbach's α. Structural validity was tested using confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). Convergent validity was tested using Pearson's correlation coefficients, which were calculated between each score of the PFI scale and burnout (the Japanese Burnout Scale: JBS), depressive symptoms (the Patient Health Questionnaire‐9), and QOL (the General Health Questionnaire‐12). Results The Cronbach's alpha was .91 in professional fulfillment, .80 in burnout: work exhaustion, .90 in burnout: interpersonal disengagement, and .89 in burnout: total score. Confirmatory factor analysis demonstrated a modest fit, and EFA yielded a three‐factor structure the same as the original PFI. The all three subscales and total score of burnout were significantly correlated with the scores of all the scales (p < .001; e.g., burnout: work exhaustion correlated emotional exhaustion in JBS, r = .71). Conclusions The Japanese version of the PFI demonstrated acceptable high internal consistency, structural validity, and convergent validity of the scale with a three‐factor structure the same as in the original PFI. The Japanese version of PFI proved to be reliable and valid for use in healthcare professionals.

Background Lung sound analysis parameters have been reported to be useful biomarkers for evaluating airway condition. We developed an automatic lung sound analysis software program for infants and children based on lung sound spectral curves of frequency and power by leveraging machine learning (ML) technology. Methods To put this software program into clinical practice, in Study 1, the reliability and reproducibility of the software program using data from younger children were examined. In Study 2, the relationship between lung sound parameters and respiratory flow (L/s) was evaluated using data from older children. In Study 3, we conducted a survey using the ATS-DLD questionnaire to evaluate the clinical usefulness. The survey focused on the history of wheezing and allergies, among healthy 3-year-old infants, and then measured lung sounds. The clinical usefulness was evaluated by comparing the questionnaire results with the results of the new lung sound parameters. Results In Studies 1 and 2, the parameters of the new software program demonstrated excellent reproducibility and reliability, and were not affected by airflow (L/s). In Study 3, infants with a history of wheezing showed lower FAP 0 and RPF 75p ( p  < 0.001 and p  = 0.025, respectively) and higher PAP 0 ( p  = 0.001) than healthy infants. Furthermore, infants with asthma/asthma-like bronchitis showed lower FAP 0 ( p  = 0.002) and higher PAP 0 ( p  = 0.001) than healthy infants. Conclusions Lung sound parameters obtained using the ML algorithm were able to accurately assess the respiratory condition of infants. These parameters are useful for the early detection and intervention of childhood asthma.

Trauma informed care (TIC) is an important approach for people who have experienced trauma. Although psychological first aid (PFA) may be effective training in TIC, no study reported an association between PFA training and TIC. This study aimed to investigate the association between PFA training and attitudes toward TIC among health care professionals in Japan. Japanese health care professionals were recruited for a survey conducted from May 21 to June 18, 2021. TIC was assessed by the Attitudes Related to Trauma Informed Care Scale 10-item short form (ARTIC-10). A question about PFA training participation was originally developed through discussion among researchers. Univariate and multiple linear regression analyses were used to examine the association between the PFA experience and ARTIC-10. In total, 484 (3.6%) health care professionals completed all questions. Among them, 77 (15.9%) had experienced PFA training. Univariate and multiple linear regression analyses showed that PFA experience (B = 0.19, 95% CI: 0.02-0.36, = 0.03; B = 0.17, 95% CI: 0.01-0.34, = 0.04) was significantly associated with ARTIC-10. This study was the first to show an association between PFA training and attitudes toward TIC among health care professionals, which was a significant result for future research on PFA training, TIC, and trauma care.

People with a strong sense of coherence (SOC) have a high ability to cope with stress and maintain good physical and mental health. The aim of this study was to investigate the relationship between depressive state, job stress, and SOC among nurses in a Japanese general hospital. A self-reporting survey was conducted among 348 female nurses in a general hospital. Job stress was measured using the Japanese version of the effort-reward imbalance (ERI) scale. Depressive state was assessed by the K6 scale. SOC was assessed with the SOC scale, which includes 29 items. Stepwise multiple regression analysis was conducted to examine factors that significantly affect depressive state. SOC, over-commitment, effort-esteem ratio, and age were significantly correlated with the depressive state (β = -0.46, P < 0.001; β = 0.27, P < 0.001; β = 0.16, P < 0.001; β = -0.10, P < 0.001, respectively). SOC may have a major influence on the depressive state among female nurses in a Japanese general hospital. From a practical perspective, health care professionals should try to enhance the SOC of nurses.

Objectives A casual relationship between temperament, job stress and depressive symptoms has not been established yet. The purpose of this study was to assess the relationships between job stress, temperament and depressive symptoms in female nurses at a Japanese general hospital. Material and Methods A self-report survey was conducted among 706 nurses. We measured job stress, temperament, and depressive symptoms using the Brief-Job Stress Questionnaire, the TEMPS-A and a screening scale of items from the Ministry of Health, Labour and Welfare of Japan. In order to examine the causal relationship between the measures the stepwise multiple regression and path analyses were used. Results: Depressive symptoms were modestly correlated with job stress (γ = −0.23–0.30). Except for hyperthymic temperament measures, the correlations between depressive symptoms and temperament types were significant and moderate (γ = 0.36–0.50). Overtime, job control as well as depressive and cyclothymic types of temperament were significantly correlated with depressive symptoms (β = 0.15, p < 0.05; β = 0.19, p < 0.01; β = 0.26, p < 0.001; β = 0.32, p < 0.001, respectively). Path-analysis revealed that depressive and cyclothymic types of temperament influenced depressive symptoms both directly (β = 0.67, p < 0.001) and indirectly via job stress (β = 0.35, p < 0.001 from temperament to job stress; β = 0.20, p < 0.05 from job stress to depressive symptoms). Irritable and anxious types of temperament and quantitative job overload did not contribute to the path-analytic model. Conclusions Health care professionals should consider temperament, especially depressive and cyclothymic types, in order to help employees cope better with job stress factors. We need further research about the effective intervention to help employees better cope with their job stress.

This study aimed to compare longitudinal change of the psychological distress of a group with psychological first aid (PFA) experience and a group without PFA experience among physicians and other healthcare professionals from before the novel coronavirus disease (COVID-19) pandemic to during the pandemic. The baseline survey was conducted in January 2020 (T1). The respondents in T1 were invited to participate in March (T2) and November 2020 (T3). Psychological distress was assessed by the Kessler 6 Scale. Participants were divided into two categories: a group with and a group without PFA experience. Participants were further divided between physicians and healthcare professionals other than physicians, because physicians are more likely to experience morally injurious events. A mixed-model repeated-measures ANOVA was conducted as an indicator of the group differences. In T1, 398 healthcare professionals participated. The longitudinal analysis of healthcare professionals other than physicians showed that psychological distress was significantly greater in the group without PFA experience than in the group with PFA experience (T1 vs. T3). This study showed psychological distress among healthcare professionals other than physicians was significantly greater in the group without PFA experience than in the group with PFA experience during the COVID-19 pandemic, but the results were not consistent among physicians.

PurposeThe objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms.MethodsWe developed machine learning models to predict the FAZ area from OCT B-scan images of eyes without retinal vascular diseases. The study involved three models: Model 1 predicted the FAZ length from B-scan images; Model 2 estimated the FAZ area from the predicted length using 1, 3, or 5 horizontal measurements; and Model 3 converted the FAZ area from pixels to mm2. The models' performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R2). The FAZ area was subsequently estimated by sequentially applying Models 1→2→3 on a new dataset.ResultsModel 1 achieved a MAE of 2.86, MSE of 17.56, and R2 of 0.87. Model 2's performance improved with the number of horizontal measurements, with the best results obtained using 5 lines (MAE: 40.36, MSE: 3129.65, R2: 0.95). Model 3 achieved a MAE of 1.52e-3, MSE of 4.0e-6, and R2 of 1.0. The accuracy of FAZ area estimation increased with the number of B-scan images used, with the correlation coefficient rising from 0.475 (1 line) to 0.596 (5 lines). Bland-Altman analysis showed improved agreement between predicted and actual FAZ areas with increasing B-scan images, evidenced by decreasing biases and narrower limits of agreement.ConclusionsThis study successfully developed machine learning models capable of predicting FAZ area from OCT B-scan images. These findings demonstrate the potential for using OCT images to predict OCTA data, particularly in populations where OCTA imaging is challenging, such as children and the elderly. Future studies could explore the developmental mechanisms of the FAZ and macula, providing new insights into retinal health across different age groups.

We have recently established an enzyme-linked immunosorbent assay (ELISA) for total ovomucoid determination, irrespective of the degree of its heat denaturation, by using a monoclonal antibody (mAb) 7D specific to the carbohydrate moiety of ovomucoid (Biosci Biothechnol Biochem, 68, 2490-2497, 2004). Two novel methods have been developed to improve the ELISA. First, its sensitivity was enhanced 100 times by using an oligoclonal cocktail of mAb 7D and two other mAbs with different epitopes as a second antibody. Second, it was shown that usage of denaturing reagents such as SDS and beta-mercaptoethanol for extraction was acceptable for ELISA within a range of stability of a first antibody on a solid phase. Properties of the oligoclonal sandwich ELISA system thus constructed were discussed in connection with allergen labeling.

Background: People with a strong sense of coherence (SOC) have a high ability to cope with stress and maintain good physical and mental health. Aims: The aim of this study was to investigate the relationship between depressive state, job stress, and SOC among nurses in a Japanese general hospital. Materials and Methods: A self-reporting survey was conducted among 348 female nurses in a general hospital. Job stress was measured using the Japanese version of the effort-reward imbalance (ERI) scale. Depressive state was assessed by the K6 scale. SOC was assessed with the SOC scale, which includes 29 items. Stepwise multiple regression analysis was conducted to examine factors that significantly affect depressive state. Results: SOC, over-commitment, effort-esteem ratio, and age were significantly correlated with the depressive state ( beta = -0.46, P < 0.001; beta = 0.27, P < 0.001; beta = 0.16, P < 0.001; beta = -0.10, P < 0.001, respectively). Conclusions: SOC may have a major influence on the depressive state among female nurses in a Japanese general hospital. From a practical perspective, health care professionals should try to enhance the SOC of nurses.