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Exposure to N 2 O 5 generated by plasma technology activates immunity in Arabidopsis through tryptophan metabolites. However, little is known about the effects of N 2 O 5 exposure on other plant species. Sweet basil synthesizes many valuable secondary metabolites in its leaves. Therefore, metabolomic analyses were performed at three different exposure levels [9.7 (Ex1), 19.4 (Ex2) and 29.1 (Ex3) μmol] to assess the effects of N 2 O 5 on basil leaves. As a result, cinnamaldehyde and phenolic acids increased with increasing doses. Certain flavonoids, columbianetin, and caryophyllene oxide increased with lower Ex1 exposure, cineole and methyl eugenol increased with moderate Ex2 exposure and l -glutathione GSH also increased with higher Ex3 exposure. Furthermore, gene expression analysis by quantitative RT-PCR showed that certain genes involved in the syntheses of secondary metabolites and jasmonic acid were significantly up-regulated early after N 2 O 5 exposure. These results suggest that N 2 O 5 exposure increases several valuable secondary metabolites in sweet basil leaves via plant defense responses in a controllable system.

Exposure to N O generated by plasma technology activates immunity in Arabidopsis through tryptophan metabolites. However, little is known about the effects of N O exposure on other plant species. Sweet basil synthesizes many valuable secondary metabolites in its leaves. Therefore, metabolomic analyses were performed at three different exposure levels [9.7 (Ex1), 19.4 (Ex2) and 29.1 (Ex3) μmol] to assess the effects of N O on basil leaves. As a result, cinnamaldehyde and phenolic acids increased with increasing doses. Certain flavonoids, columbianetin, and caryophyllene oxide increased with lower Ex1 exposure, cineole and methyl eugenol increased with moderate Ex2 exposure and L-glutathione GSH also increased with higher Ex3 exposure. Furthermore, gene expression analysis by quantitative RT-PCR showed that certain genes involved in the syntheses of secondary metabolites and jasmonic acid were significantly up-regulated early after N O exposure. These results suggest that N O exposure increases several valuable secondary metabolites in sweet basil leaves via plant defense responses in a controllable system.

Objectives To compare the [ 18 F]FDG PET/CT findings of untreated sarcoidosis and malignant lymphoma (ML) and develop convolutional neural network (CNN) models to differentiate between these diseases using maximum intensity projection (MIP) [ 18 F]FDG PET images. Methods We retrospectively collected data on consecutive patients newly diagnosed with sarcoidosis and ML who underwent [ 18 F]FDG PET/CT before treatment. Two nuclear radiologists reviewed the images. CNN models were created using MIP PET images and evaluated with k -fold cross-validation. The points of interest were visualized using gradient-weighted class activation mapping (Grad-CAM). Results A total of 56 patients with sarcoidosis and 62 patients with ML were included. Patients with sarcoidosis had more prominent FDG accumulation in the mediastinal lymph nodes and lung lesions, while those with ML had more prominent accumulation in the cervical lymph nodes (all p < 0.001). For the mediastinal lymph nodes, sarcoidosis patients had significant FDG accumulation in the level 2, 4, 7, and 10 lymph nodes (all p < 0.01). Otherwise, the accumulation in ML patients tended to be in the level 1 lymph nodes ( p = 0.08). The CNN model using frontal and lateral MIP images achieved an average accuracy of 0.890 (95% CI: 0.804–0.977), a sensitivity of 0.898 (95% CI: 0.782–1.000), a specificity of 0.907 (95% CI: 0.799–1.000), and an area under the curve of 0.963 (95% CI: 0.899–1.000). Grad-CAM showed that the model focused on the sites of abnormal FDG accumulation. Conclusions CNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML. Clinical relevance statement We developed a CNN model using MIP images of [ 18 F]FDG PET/CT to distinguish between sarcoidosis and malignant lymphoma. It achieved high performance and could be useful in diagnosing diseases with involvement across organs and lymph nodes. Key Points • There are differences in FDG distribution when comparing whole-body [ 18 F]FDG PET/CT findings in patients with sarcoidosis and malignant lymphoma before treatment. • Convolutional neural networks, a type of deep learning technique, trained with maximum-intensity projection PET images from two angles showed high performance. • A deep learning model that utilizes differences in FDG distribution may be helpful in differentiating between diseases with lesions that are characteristically widespread among organs and lymph nodes.

Objectives Although gender stereotypes regarding paid work and unpaid work are changing, most wives are responsible for taking care of the family and home in Japan. It is unclear how time spent on housework and childcare has changed between working men and women during the COVID‐19 pandemic in Japan. The purpose of this study is to investigate how working men and women’s responsibilities for housework and childcare changed during the COVID‐19 pandemic in Japan depending on work hours, job type, the number of employees in the workplace, and frequency of telecommuting. Methods A cross‐sectional analysis (N = 14,454) was conducted using data from an Internet monitoring study (CORoNa Work Project), which was conducted in December 2020. A multilevel logistic model with nested prefectures of residence was conducted to estimate the odds ratio (OR) for change in time devoted to housework and childcare among men and women adjusting for age, household income, presence of spouse who work, work hours, job type, the number of employees in the workplace, frequency of telecommuting, and the incidence rate of COVID‐19 by prefecture. Results More women tended to perceive that their time of housework and/or childcare had been changed (increased housework: OR 1.92, 95% CI [1.71–2.16], P < .001; decreased workhours: 1.66 (1.25–2.19), P < .001: increased childcare: OR 1.58, 95% CI [1.29–1.92], P < .001; decreased childcare: 1.11 (0.62–2.00), P = .719). Conclusions The time spent by women on housework and childcare changed significantly compared to men during the COVID‐19 pandemic in Japan.

Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775–0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830–0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829–0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.

Objectives Little is known about workplace measures against coronavirus disease 2019 (COVID‐19) in Japan during the winter of 2020, especially in micro‐, small‐, and medium‐sized enterprises (MSMEs). This study aimed to provide an overview of the current situation of anti‐COVID‐19 measures in Japanese enterprises during the winter, considering company size. Methods This study was an Internet‐based nationwide cross‐sectional study. Individuals who were registered as full‐time workers were invited to participate in the survey. Data were collected using an online self‐administered questionnaire in December 2020. The chi‐squared test for trend was performed to calculate the P‐value for trend for each workplace measure across company sizes. Results For the 27 036 participants, across company sizes, the most prevalent workplace measure was encouraging mask wearing at work, followed by requesting that employees refrain from going to work when ill and restricting work‐related social gatherings and entertainment. These measures were implemented by approximately 90% of large‐scale enterprises and by more than 40% of micro‐ and small‐scale enterprises. In contrast, encouraging remote working was implemented by less than half of large‐scale enterprises and by around 20% of micro‐ and small‐scale enterprises. There were statistically significant differences in all workplace measures by company size (all P < .001). Conclusions We found that various responses to COVID‐19 had been taken in workplaces. However, some measures, including remote working, were still not well‐implemented, especially in smaller enterprises. The findings suggest that occupational health support for MSMEs is urgently needed to mitigate the current wave of COVID‐19.

SMARCA4-deficient thoracic sarcoma is a rare tumor typically presenting as a mediastinal mass. The prognosis is estimated to be poor, and no effective treatment has been established. We present a case of a 76-year-old man who was diagnosed with SMARCA4-deficient thoracic sarcoma. The provisional diagnosis was carcinoma of unknown primary but subsequently corrected to SMARCA4-deficient thoracic sarcoma based on the panel-based cancer gene screening and immunohistochemistry. Cytotoxic chemotherapy as the first- and second-line did not reveal enough therapeutic effects but third-line therapy using nivolumab showed marked tumor regression, which was sustained. This is the first case report of SMARCA4-deficient thoracic sarcoma showing a good response to nivolumab. Immune checkpoint inhibitor might be therapeutic candidates for this type of tumor.

To compare the [18F]FDG PET/CT findings of untreated sarcoidosis and malignant lymphoma (ML) and develop convolutional neural network (CNN) models to differentiate between these diseases using maximum intensity projection (MIP) [18F]FDG PET images.OBJECTIVESTo compare the [18F]FDG PET/CT findings of untreated sarcoidosis and malignant lymphoma (ML) and develop convolutional neural network (CNN) models to differentiate between these diseases using maximum intensity projection (MIP) [18F]FDG PET images.We retrospectively collected data on consecutive patients newly diagnosed with sarcoidosis and ML who underwent [18F]FDG PET/CT before treatment. Two nuclear radiologists reviewed the images. CNN models were created using MIP PET images and evaluated with k-fold cross-validation. The points of interest were visualized using gradient-weighted class activation mapping (Grad-CAM).METHODSWe retrospectively collected data on consecutive patients newly diagnosed with sarcoidosis and ML who underwent [18F]FDG PET/CT before treatment. Two nuclear radiologists reviewed the images. CNN models were created using MIP PET images and evaluated with k-fold cross-validation. The points of interest were visualized using gradient-weighted class activation mapping (Grad-CAM).A total of 56 patients with sarcoidosis and 62 patients with ML were included. Patients with sarcoidosis had more prominent FDG accumulation in the mediastinal lymph nodes and lung lesions, while those with ML had more prominent accumulation in the cervical lymph nodes (all p < 0.001). For the mediastinal lymph nodes, sarcoidosis patients had significant FDG accumulation in the level 2, 4, 7, and 10 lymph nodes (all p < 0.01). Otherwise, the accumulation in ML patients tended to be in the level 1 lymph nodes (p = 0.08). The CNN model using frontal and lateral MIP images achieved an average accuracy of 0.890 (95% CI: 0.804-0.977), a sensitivity of 0.898 (95% CI: 0.782-1.000), a specificity of 0.907 (95% CI: 0.799-1.000), and an area under the curve of 0.963 (95% CI: 0.899-1.000). Grad-CAM showed that the model focused on the sites of abnormal FDG accumulation.RESULTSA total of 56 patients with sarcoidosis and 62 patients with ML were included. Patients with sarcoidosis had more prominent FDG accumulation in the mediastinal lymph nodes and lung lesions, while those with ML had more prominent accumulation in the cervical lymph nodes (all p < 0.001). For the mediastinal lymph nodes, sarcoidosis patients had significant FDG accumulation in the level 2, 4, 7, and 10 lymph nodes (all p < 0.01). Otherwise, the accumulation in ML patients tended to be in the level 1 lymph nodes (p = 0.08). The CNN model using frontal and lateral MIP images achieved an average accuracy of 0.890 (95% CI: 0.804-0.977), a sensitivity of 0.898 (95% CI: 0.782-1.000), a specificity of 0.907 (95% CI: 0.799-1.000), and an area under the curve of 0.963 (95% CI: 0.899-1.000). Grad-CAM showed that the model focused on the sites of abnormal FDG accumulation.CNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML.CONCLUSIONSCNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML.We developed a CNN model using MIP images of [18F]FDG PET/CT to distinguish between sarcoidosis and malignant lymphoma. It achieved high performance and could be useful in diagnosing diseases with involvement across organs and lymph nodes.CLINICAL RELEVANCE STATEMENTWe developed a CNN model using MIP images of [18F]FDG PET/CT to distinguish between sarcoidosis and malignant lymphoma. It achieved high performance and could be useful in diagnosing diseases with involvement across organs and lymph nodes.• There are differences in FDG distribution when comparing whole-body [18F]FDG PET/CT findings in patients with sarcoidosis and malignant lymphoma before treatment. • Convolutional neural networks, a type of deep learning technique, trained with maximum-intensity projection PET images from two angles showed high performance. • A deep learning model that utilizes differences in FDG distribution may be helpful in differentiating between diseases with lesions that are characteristically widespread among organs and lymph nodes.KEY POINTS• There are differences in FDG distribution when comparing whole-body [18F]FDG PET/CT findings in patients with sarcoidosis and malignant lymphoma before treatment. • Convolutional neural networks, a type of deep learning technique, trained with maximum-intensity projection PET images from two angles showed high performance. • A deep learning model that utilizes differences in FDG distribution may be helpful in differentiating between diseases with lesions that are characteristically widespread among organs and lymph nodes.