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.

Background and Objectives: Cigarette smoking is a major risk factor for atherosclerosis.Nicotine, a crucial constituent of tobacco, contributes to atherosclerosis development and progression. However, evidence of the association between nicotine and neointima formation is limited. We aimed to evaluate whether nicotine enhances neointimal hyperplasia in the native epicardial coronary arteries of pigs after percutaneous coronary intervention (PCI) with drug-eluting stents (DES). Methods: After coronary angiography (CAG) and quantitative coronary angiography (QCA), we implanted 20 DES into 20 pigs allocated to 2 groups: no-nicotine (n=10) and nicotine (n=10) groups. Post-PCI CAG and QCA were performed immediately. Follow-up CAG, QCA, optical coherence tomography (OCT), and histopathological analyses were performed 2 months post-PCI. Results: Despite intergroup similarities in the baseline QCA findings, OCT analysis showed that the nicotine group had a smaller mean stent and lumen areas, a larger mean neointimal area, greater percent area stenosis, and higher peri-strut fibrin and inflammation scores than the no-nicotine group. In immunofluorescence analysis, the nicotine group displayed higher expression of CD68 and α-smooth muscle actin but lower CD31 expression than the no-nicotine group. Conclusions Nicotine inhibited re-endothelialization and promoted inflammation and NIH after PCI with DES in a porcine model.

Background and Objectives: Outcomes of deferring percutaneous coronary intervention (PCI) without invasive physiologic assessment for intermediate coronary lesions is uncertain.We sought to compare long-term outcomes between medical treatment and PCI of intermediate lesions without invasive physiologic assessment. Methods: A total of 899 patients with intermediate coronary lesions between 50% and 70% diameter-stenosis were randomized to the conservative group (n=449) or the aggressive group (n=450). For intermediate lesions, PCI was performed in the aggressive group, but was deferred in the conservative group. The primary endpoint was major adverse cardiac events (MACE, a composite of all-cause death, myocardial infarction [MI], or ischemia-driven any revascularization) at 3 years. Results: The number of treated lesions per patient was 0.8±0.9 in the conservative group and 1.7±0.9 in the aggressive group (p=0.001). At 3 years, the conservative group had a significantly higher incidence of MACE than the aggressive group (13.8% vs. 9.3%; hazard ratio [HR], 1.49; 95% confidence interval [CI], 1.00–2.21; p=0.049), mainly driven by revascularization of target intermediate lesion (6.5% vs. 1.1%; HR, 5.69; 95% CI, 2.20–14.73;p<0.001). Between 1 and 3 years after the index procedure, compared to the aggressive group, the conservative group had significantly higher incidence of cardiac death or MI (3.2% vs.0.7%; HR, 4.34; 95% CI, 1.24–15.22; p=0.022) and ischemia-driven any revascularization. Conclusions For intermediate lesions, medical therapy alone, guided only by angiography, was associated with a higher risk of MACE at 3 years compared with performing PCI, mainly due to increased revascularization.

Background: The oxygen reserve index (ORi) noninvasively measures oxygen levels within the mild hyperoxia range. To evaluate whether a degree of increase in the ORi during preoxygenation for general anesthesia is associated with the occurrence of postoperative pulmonary complications (PPCs). Methods: We enrolled 154 patients who underwent preoperative pulmonary function tests and were scheduled for elective surgery under general anesthesia. We aimed to measure the increase in ORi during preoxygenation before general anesthesia and analyze its association with PPCs. Results: PPCs occurred in 76 (49%) participants. Multivariate logistic regression analysis revealed that the three-minute preoxygenation ORi was significantly associated with PPCs (Odds ratio [OR]: 0.02, 95% CI [0.00–0.16], P < 0.001). The areas under the curve (AUC [95% CI]) in the receiver operating characteristic curve analysis for the three-minute preoxygenation ORi for PPCs were 0.64 (0.55–0.73). After a subgroup analysis, multivariate logistic regression showed that the three-minute preoxygenation ORi was significantly associated with PPCs among patients who underwent thoracic surgery (OR: 0.01, 95% CI [0.00–0.19], P = 0.006). The AUC of the three-minute preoxygenation ORi for PPCs was 0.72 (0.57–0.86) in patients who underwent thoracic surgery. Conclusions A low ORi measured after 3 min of preoxygenation for general anesthesia was associated with an increased risk of PPCs, including those undergoing thoracic surgery. This study demonstrated the potential of ORi, measured after oxygen administration, as a tool for evaluating lung function that complements traditional lung function tests and scoring systems.

Purpose: The opacity of zirconia sometimes requires a veneering material; thus lithium disilicate, a veneer material with excellent strength, can be used. This study investigated the fracture resistance of zirconia–lithium disilicate (Zr-LS2) bi-layered crowns according to the design of the substructure. Materials and Methods: Five groups of posterior Zr-LS2 restorations (Zirtooth and Amber LiSi-POZ) were fabricated with different zirconia substructure coverage (control group, groups half occlusal zirconia coverage; 1/2OZ, and three-quarter occlusal zirconia coverage; 3/4OZ) and margin designs (control group, groups collar margin; C-M, and collarless margin; Cl-M). All restorations were cemented with self-adhesive resin cement followed by 24-h water storage and thermocycling (10,000 cycles, 5°C and 55°C). The fracture load was measured, and failure mode analysis, fractography, and elemental analysis were performed. The one-way analysis of variance and Fisher’s exact test were performed for statistical analyses (α=.05). Results: A significant difference was found in the fracture load of Zr-LS2 restorations according to the zirconia coverage of the occlusal area and margin design (P<.05). Group 3/4OZ was significantly larger than the control group C and 1/2OZ (P<.05). The C-M group had greater fracture loads based on margin design than the control group C and Cl-M (P<.05). Conclusion The fracture resistance of posterior Zr-LS2 restorations increased with the zirconia coverage, occlusal thickness, and collar margin.

Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.

Purpose: The opacity of zirconia sometimes requires a veneering material; thus lithium disilicate, a veneer material with excellent strength, can be used. This study investigated the fracture resistance of zirconia–lithium disilicate (Zr-LS2) bi-layered crowns according to the design of the substructure. Materials and Methods: Five groups of posterior Zr-LS2 restorations (Zirtooth and Amber LiSi-POZ) were fabricated with different zirconia substructure coverage (control group, groups half occlusal zirconia coverage; 1/2OZ, and three-quarter occlusal zirconia coverage; 3/4OZ) and margin designs (control group, groups collar margin; C-M, and collarless margin; Cl-M). All restorations were cemented with self-adhesive resin cement followed by 24-h water storage and thermocycling (10,000 cycles, 5°C and 55°C). The fracture load was measured, and failure mode analysis, fractography, and elemental analysis were performed. The one-way analysis of variance and Fisher’s exact test were performed for statistical analyses (α=.05). Results: A significant difference was found in the fracture load of Zr-LS2 restorations according to the zirconia coverage of the occlusal area and margin design (P<.05). Group 3/4OZ was significantly larger than the control group C and 1/2OZ (P<.05). The C-M group had greater fracture loads based on margin design than the control group C and Cl-M (P<.05). Conclusion The fracture resistance of posterior Zr-LS2 restorations increased with the zirconia coverage, occlusal thickness, and collar margin.

Background: This study aimed to identify key priorities for the development of guidelines for information and communication technology (ICT)-based patient education tailored to the needs of patients with rheumatic diseases (RDs) in the Republic of Korea, based on expert consensus. Methods: A two-round modified Delphi study was conducted with 20 rheumatology, patient education, and digital health literacy experts. A total of 35 items covering 7 domains and 18 subdomains were evaluated. Each item was evaluated for its level of importance, and the responses were rated on a 4-point Likert scale. Consensus levels were defined as “high” (interquartile range [IQR] ≤ 1, agreement ≥ 80%, content validity ratio [CVR] ≥ 0.7), "Moderate" (IQR ≥ 1, agreement 50–79%, CVR 0.5–0.7), and "Low" (IQR > 1, agreement < 50%, CVR < 0.5). Results: Strong consensus was reached for key priorities for developing guidelines in areas such as health literacy, digital health literacy, medical terminology, user interface, and user experience design for mobile apps. Chatbot use and video (e.g., YouTube) also achieved high consensus, whereas AI-powered platforms such as ChatGPT showed moderate-to-high agreement. Telemedicine was excluded because of insufficient consensus. Conclusion The key priorities identified in this study provide a foundation for the development of ICT-based patient education guidelines for RDs in the Republic of Korea.Future efforts should focus on integrating digital tools into clinical practice to enhance patient engagement and improve clinical outcomes.