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: 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: 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.

This study employed Q-methodology to explore frames of reference of nursing home practitioners in managing discomfort of residents with dementia. Methods: In-depth interviews were conducted with eight nursing home practitioners, all of whom had experience caring for residents with dementia. Additionally, a literature review on discomfort, its assessment, and interventions was conducted. Thirty-four practitioners as a P-sample, including nurses, care workers, social workers, and physical therapists, classified 34 Q-samples through Q-sorting. Data analysis was performed using the PQ-method 2.35 program. Results were interpreted based on factor arrays, post-interview data, and distribution of the P-sample. Results: Four Q-factors were identified as practitioners’ frames of references for discomfort management of nursing home residents: exploring possible management based on a synthesis of related clues, an individual assessment-based approach using possible assumptions, linking subtle changes to management direction based on a trusting relationship, and configuration-centered management direction using objective data. Conclusion: Utilizing nursing home practitioners’ frames of reference for managing demented residents’ discomfort can be used for developing strategies that can be used in practitioners’ education and promoting practice that can reduce discomfort of nursing home residents.

Purpose: The Korean Oral Biobank Network (KOBN) collects, stores, and provides oral samples for research. Quality control (QC) of biospecimens is necessary to ensure that they meet the basic prerequisites before being sent to researchers. This study presents the standard operating procedures (SOPs) for the QC of biospecimens. Methods: QC methods using molecular genetic techniques according to sample types, including teeth, blood, oral soft tissue, oral tissue-derived cells, saliva, mouth rinse solution, dental plaque, and gingival crevicular fluid, are described in detail. Results: The KOBN established SOP for oral biospecimen QC and assessment methods. Conclusions To ensure a stable supply of high-quality biospecimens for researchers, regular QC checks should be carried out according to the SOP following the specifications of the Korea Biobank Network under the Korea Disease Control and Prevention Agency.

Visfatin, an adipokine secreted by cells, is crucial for intracellular nicotinamide adenine dinucleotide + biosynthesis.Extracellularly, visfatin plays diverse roles in inflammatory conditions, including obesity, which is closely linked to osteoclastogenesis. We previously showed that visfatin enhances receptor activator of nuclear factor kappa-B ligand (RANKL)-induced osteoclastogenesis in bone marrow-derived macrophages. However, its enzymatic activity during this process is poorly understood. Here, we investigated visfatin’s effects on RANKL-induced osteoclast differentiation. Our results demonstrate that visfatin promotes this differentiation, an effect inhibited by FK866, an inhibitor of visfatin’s enzymatic activity. Furthermore, FK866 also inhibited RANKL-induced osteoclast differentiation.These findings suggest that inhibiting visfatin’s enzymatic activity modulates osteoclast differentiation. Thus, visfatin plays an important role in osteoclastogenesis, both intracellularly and extracellularly, and FK866 has therapeutic potential for diseases characterized by imbalanced osteoclast formation, such as osteoporosis and periodontitis.

Cardiac organoids have emerged as invaluable tools for assessing the impact of diverse substances on heart function.This report introduces guidelines for general requirements for manufacturing cardiac organoids and conducting cardiac organoid-based assays, encompassing protocols, analytical methodologies, and ethical considerations. In the quest to employ recently developed three-dimensional cardiac organoid models as substitutes for animal testing, it becomes imperative to establish robust criteria for evaluating organoid quality and conducting toxicity assessments. This guideline addresses this need, catering to regulatory requirements, and describes common standards for organoid quality and toxicity assessment methodologies, commensurate with current technological capabilities. While acknowledging the dynamic nature of technological progress and the potential for future comparative studies, this guideline serves as a foundational framework. It offers a comprehensive approach to standardized cardiac organoid testing, ensuring scientific rigor, reproducibility, and ethical integrity in investigations of cardiotoxicity, particularly through the utilization of human pluripotent stem cell-derived cardiac organoids.

Background: Dental hygienists have a significant risk of infection due to occupational injuries caused by needles and sharpinstruments. This study aimed to evaluate the current status of needle and “sharp-instrument injuries” among dental hygienists and to propose improved preventive guidelines. Methods: A total of 251 dental hygienists completed an online survey between August 1, 2023 and September 2, 2023. Data from245 respondents were analyzed using IBM SPSS version 20, using independent t-tests and one-way analysis of variance to assess the frequency of injuries and their correlation with job characteristics. Results: Among the 251 dental hygienists, 77.6% had experienced needle or sharp-instrument injuries, with an average of 4.97incidents per person. Infection prevention education significantly reduced the number of injuries, and participants with education exhibited better infection control practices than those without. Most injuries occurred during “instrument cleaning or maintenance” and “anesthesia preparation or disposal,” with “scalers, probes, and curettes” being the main culprits. Hands were the most frequently injured body parts. Conclusion Preventive measures, continuous education, and improved guidelines are required to create a safer dental workingenvironment.