Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study evaluated the nutrition and food intake status among adults in Jeju, Republic of Korea, based on their sociodemographic characteristics and obesity. Methods: Nine hundred and forty-nine adults aged 19 years or older were included based on the 2022 Jeju Nutrition and Physical Activity Survey data. A dietary assessment was conducted using a one-day, 24-hour recall method, and the nutrition status was evaluated using the 2020 Dietary Reference Intakes for Koreans. The sociodemographic status included sex, age, education, and household income, and obesity was defined as a body mass index (BMI) of ≥ 25 kg/m 2 . Results: The mean age of the subjects was 49.8 years, and the overall prevalence of obesity was 48.4% (59.5% for males and 37.2% for females). The top three insufficient nutrients consumed at less than the estimated average requirement were calcium (77.7%), vitamin A (77.3%), and vitamin C (61.3%), while 40.9% of the subjects consumed fat as more than 30% of their total energy intake. The sodium intake was approximately 1.5 times higher than the chronic disease risk reduction intake level. In terms of food groups, the participants consumed more meat (148.2 g/day), seafood (69.0 g/day), potatoes, and starches (41.6 g/day) but consumed fewer vegetables (214.3 g/day) and less dairy (62.0 g/day) than Korean adults. The nutritional status and food intake patterns differed by the sociodemographic status. In addition, the obese group consumed more poultry and beverages than the non-obese group (p < 0.05 for all). Conclusion Adults in Jeju under-consume essential nutrients while over-consuming certain nutrients, such as fat and sodium. The nutritional status also varied according to the sociodemographic characteristics. These findings suggest that sociodemographic factors should be considered carefully when developing nutritional policies and programs to improve the nutritional status and address obesity among adults in Jeju.

Purpose: This study aimed to improve the antioxidant activities of sweet persimmon peel extracts using supercritical carbon dioxide (SFE-CO2 ) as a green extraction (GE) technology, as part of upcycling efforts. It also aimed to demonstrate the effectiveness of GE as an ecofriendly extraction method by comparing it with conventional extraction (CE) techniques. Methods: Sweet persimmon peel extracts were obtained using CE (hot water at 80°C for 6 h or 95% ethanol at room temperature for 24 hours) and SFE-CO2 extraction (50°C for 2 hours, with pressures ranging from 100 to 250 bar). Antioxidant activities (2,2-diphenyl-1-picrylhydrazyl [DPPH] radical scavenging activity and tannin content) were analyzed to evaluate and compare the antioxidant extraction efficiency across different extraction methods. Results: In the CE extraction method, the 95% ethanol extract exhibited 1.2 times higher DPPH radical scavenging activity and 1.5 times higher tannin content than that of the hot water extract. In the SFE-CO2 extraction method, antioxidant activities increased with increasing pressure (100–250 bar), as higher pressures enhanced antioxidant activities and extraction efficiency. At 250 bar, the SFE-CO2 extracts demonstrated 1.6 times higher DPPH radical scavenging activity and 2.0 times higher tannin content than that of the hot water extract, and 1.3 times higher DPPH scavenging activity and tannin content than that of the 95% ethanol extract. These findings highlight the superior efficiency of extraction using the SFE-CO2 method. Conclusion This study demonstrated that SFE-CO2 was an efficient and eco-friendly method for extracting antioxidants from sweet persimmon peels, surpassing conventional methods.It underscores the potential of SFE-CO2 for the sustainable upcycling of sweet persimmon byproducts and the promotion of green technologies to enhance antioxidant activities.

Objectives: . The presence of extrathyroidal extension (ETE) in patients with differentiated thyroid cancer (DTC) serves as a significant prognostic indicator. Consequently, the staging of DTC is categorized into extensive ETE and gross ETE that solely impacts the strap muscles (gross strap muscle invasion [gSMI]). However, there is a lack of sufficient evidence concerning the relationship between gSMI and prognosis, particularly in terms of tumor size. Methods: . Relevant literature was searched in Medline, Embase, Cochrane Library, and KoreaMed. All procedures were conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and carried out by two independent reviewers. The meta-analysis utilized a random-effects model to account for the diversity of the studies. Risk of Bias for Nonrandomized Studies (RoBANS) version 2.0, an evaluation tool for non-randomized studies, was employed to assess the quality of the selected research. Clinical data from observational studies that examined the relationship between the degree of ETE and prognosis were gathered, and a meta-analysis was conducted. Results: . Eighteen observational studies were included in this analysis. Subgroup analyses were conducted for each outcome. The findings revealed that the recurrence rate (odds ratio [OR], 2.498), disease-specific mortality (risk ratio [RR], 2.984), overall mortality (RR, 1.361), and lymph node (LN) metastasis (OR, 5.355) were significantly higher in patients with gSMI than in those without ETE. However, when the analysis was restricted to tumors measuring 4 cm or smaller, no significant differences in prognostic outcomes were observed, with the exception of LN metastasis. Conclusion . gSMI negatively impacts prognosis; however, this correlation diminishes with smaller tumor sizes. Thus, a more cautious approach is warranted during the treatment process.

Purpose: This study aimed to compare and identify factors affecting delirium nursing stress among nurses in comprehensive nursing care service wards and general wards. Methods: Using structured questionnaires, data were collected from 230 nurses working in two tertiary university hospitals. Data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson's correlation coefficient, and multiple regression analysis using SPSS/WIN 27.0. Results: The average delirium nursing stress score for nurses in comprehensive nursing care service wards was 2.98±0.30 out of 4 points, which was significantly higher than 2.89±0.29 points for nurses in general wards (t=2.17, p=.031).Factors influencing delirium nursing stress among comprehensive nursing care service ward nurses included nursing work environment (β=-.58, p<.001) and conflicts with medical staff and other departments related to delirium patients (β=.24, p=.006), explaining power of 44% (F=12.13, p<.001). For general ward nurses, the nursing work environment(β=-.39, p<.001) was the main influencing factor, explaining power of 17% (F=3.93, p<.001). Conclusion Both types of nurses require improvements in their work environment. Strategies to reduce conflict between medical staff and other departments are essential in comprehensive nursing care service wards.