Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.

Purpose: Compassion is integral to nursing, yet the concept of compassionate care is not thoroughly understood. This study aimed to clarify the nature of compassionate care among Korean clinical nurses using a hybrid model. Methods: This study utilized a mixed methods approach involving hybrid concept analysis to explore the nature and attributes of compassionate care, encompassing both theoretical and empirical stages. In the theoretical stage, the domains, attributes, and a preliminary definition of compassionate care were formulated. In the fieldwork stage, in-depth interviews with 18 nurses were conducted to gather insights, which were integrated in the final stage. Results: Compassionate care was categorized into three domains: cognitive, relational, and behavioral, with 11 defining attributes. It was defined as the capacity to recognize individual patient needs, engage empathetically with their suffering, establish trust through emotional connection, and deliver therapeutic partnership, specialized, personalized, ethical, and holistic care. Conclusion This study advances the understanding of compassionate care in nursing by providing a multidimensional framework. It lays the groundwork for future research and practical applications, emphasizing the need for measurement tools and strategies to promote compassionate care among clinical nurses.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.

Purpose: Compassion is integral to nursing, yet the concept of compassionate care is not thoroughly understood. This study aimed to clarify the nature of compassionate care among Korean clinical nurses using a hybrid model. Methods: This study utilized a mixed methods approach involving hybrid concept analysis to explore the nature and attributes of compassionate care, encompassing both theoretical and empirical stages. In the theoretical stage, the domains, attributes, and a preliminary definition of compassionate care were formulated. In the fieldwork stage, in-depth interviews with 18 nurses were conducted to gather insights, which were integrated in the final stage. Results: Compassionate care was categorized into three domains: cognitive, relational, and behavioral, with 11 defining attributes. It was defined as the capacity to recognize individual patient needs, engage empathetically with their suffering, establish trust through emotional connection, and deliver therapeutic partnership, specialized, personalized, ethical, and holistic care. Conclusion This study advances the understanding of compassionate care in nursing by providing a multidimensional framework. It lays the groundwork for future research and practical applications, emphasizing the need for measurement tools and strategies to promote compassionate care among clinical nurses.