Purpose: To investigate changes in corneal endothelial cell (CEC) by cumulative dissipated energy (CDE) during phacoemulsification. Methods: Based on the degree of nucleus sclerosis (NS), changes in CECs were compared preoperatively and at 1 and 3 months postoperatively in 67 eyes that underwent phacoemulsification. Results: The mean CDE used during surgery was 4.30 ± 2.31. A comparison of the surgical measurements before and 1 month after surgery revealed significant differences in the cell density (CD) and coefficient of variation (CV) (p < 0.001, 0.011, respectively). The CD showed significant differences among NS grades 2–6 (p < 0.001). The CDE increased significantly with higher NS grades (r = 0.809, p < 0.001). Within the same NS grade, there was a positive correlation between higher CDE and greater CEC loss (r = 0.559, p = 0.001). CD changes were significantly associated with increasing CDE (r = 0.612, p < 0.001). The CD loss also increased significantly from NS2 to NS6 (p < 0.001). At 3 months postoperatively, surgical measurements revealed a significant decrease in CD with increasing NS grade (p = 0.010). Conclusions CDE increased with higher NS grades and there was a positive correlation between CDE and CEC loss. Therefore, surgeons should plan surgical techniques preoperatively to minimize CDE as NS increases. Additionally, it is important to assess CEC damage postoperatively based on surgical measurements in patients with high intraoperative CDE.

Purpose: To investigate changes in corneal endothelial cell (CEC) by cumulative dissipated energy (CDE) during phacoemulsification. Methods: Based on the degree of nucleus sclerosis (NS), changes in CECs were compared preoperatively and at 1 and 3 months postoperatively in 67 eyes that underwent phacoemulsification. Results: The mean CDE used during surgery was 4.30 ± 2.31. A comparison of the surgical measurements before and 1 month after surgery revealed significant differences in the cell density (CD) and coefficient of variation (CV) (p < 0.001, 0.011, respectively). The CD showed significant differences among NS grades 2–6 (p < 0.001). The CDE increased significantly with higher NS grades (r = 0.809, p < 0.001). Within the same NS grade, there was a positive correlation between higher CDE and greater CEC loss (r = 0.559, p = 0.001). CD changes were significantly associated with increasing CDE (r = 0.612, p < 0.001). The CD loss also increased significantly from NS2 to NS6 (p < 0.001). At 3 months postoperatively, surgical measurements revealed a significant decrease in CD with increasing NS grade (p = 0.010). Conclusions CDE increased with higher NS grades and there was a positive correlation between CDE and CEC loss. Therefore, surgeons should plan surgical techniques preoperatively to minimize CDE as NS increases. Additionally, it is important to assess CEC damage postoperatively based on surgical measurements in patients with high intraoperative CDE.

Purpose: To investigate changes in corneal endothelial cell (CEC) by cumulative dissipated energy (CDE) during phacoemulsification. Methods: Based on the degree of nucleus sclerosis (NS), changes in CECs were compared preoperatively and at 1 and 3 months postoperatively in 67 eyes that underwent phacoemulsification. Results: The mean CDE used during surgery was 4.30 ± 2.31. A comparison of the surgical measurements before and 1 month after surgery revealed significant differences in the cell density (CD) and coefficient of variation (CV) (p < 0.001, 0.011, respectively). The CD showed significant differences among NS grades 2–6 (p < 0.001). The CDE increased significantly with higher NS grades (r = 0.809, p < 0.001). Within the same NS grade, there was a positive correlation between higher CDE and greater CEC loss (r = 0.559, p = 0.001). CD changes were significantly associated with increasing CDE (r = 0.612, p < 0.001). The CD loss also increased significantly from NS2 to NS6 (p < 0.001). At 3 months postoperatively, surgical measurements revealed a significant decrease in CD with increasing NS grade (p = 0.010). Conclusions CDE increased with higher NS grades and there was a positive correlation between CDE and CEC loss. Therefore, surgeons should plan surgical techniques preoperatively to minimize CDE as NS increases. Additionally, it is important to assess CEC damage postoperatively based on surgical measurements in patients with high intraoperative CDE.

Bordetella hinzii is a nonfermenting, gram-negative rod and a rare opportunistic pathogen that can cause respiratory infections, bacteremia, and cholangitis. Here, we report the first case of bacteremia caused by B. hinzii in Korea. A 59-year-old man was admitted for the biopsy of a mass lesion in the left lower lobe, which was detected during a health screening. The blood cultures collected from the patient with high fever (> 39℃), which developed 4 hours after the biopsy, yielded gram-negative rods. The gram-negative bacilli were identified as B. hinzii using matrix-assisted laser desorption ionization time-of-flight mass spectrometry and PCR sequencing of the 16S rRNA gene. After 9 days of antimicrobial treatment with ampicillin/sulbactam, piperacillin/tazobactam, or meropenem, the patient improved and was discharged.

Purpose: Myosteatosis, defined as fat infiltration in muscle tissue, has been linked to poor outcomes in various cancers. However, the prognostic impact of myosteatosis on renal cell carcinoma (RCC) remains poorly understood. This study evaluated the predictive value of myosteatosis based on an artificial intelligence (AI)-driven computed tomography (CT) analysis in patients with localized RCC who underwent partial nephrectomy. Materials and Methods: This retrospective study included 170 patients with localized RCC who underwent partial nephrectomy at a single institution between 2011 and 2017. Myosteatosis was assessed on CT scans using an AI-based tool. The patients were categorized into 2 groups according to the presence or absence of myosteatosis. The clinical outcomes, including disease-free survival (DFS), were compared to determine the prognostic significance of myosteatosis. Results: Of 170 patients, 36 (21.2%) were diagnosed with myosteatosis. These patients were older and had a higher body mass index. The myosteatosis group had a higher proportion of females than the no myosteatosis group. Lymphovascular invasion and tumor necrosis were prevalent pathological features in patients with myosteatosis. Kaplan-Meier analysis demonstrated that myosteatosis was associated with significantly shorter DFS (p<0.05). Multivariate analysis confirmed that myosteatosis independently predicted adverse outcomes in patients with localized RCC. Conclusion AI-based CT analysis of myosteatosis is a reliable method for improving the risk stratification of patients with localized RCC. Patients with myosteatosis demonstrate poor pathological features and shorter DFS. These findings highlight the potential of AI-driven body composition analysis to refine prognostic models and personalized treatment strategies.

Purpose: Myosteatosis, defined as fat infiltration in muscle tissue, has been linked to poor outcomes in various cancers. However, the prognostic impact of myosteatosis on renal cell carcinoma (RCC) remains poorly understood. This study evaluated the predictive value of myosteatosis based on an artificial intelligence (AI)-driven computed tomography (CT) analysis in patients with localized RCC who underwent partial nephrectomy. Materials and Methods: This retrospective study included 170 patients with localized RCC who underwent partial nephrectomy at a single institution between 2011 and 2017. Myosteatosis was assessed on CT scans using an AI-based tool. The patients were categorized into 2 groups according to the presence or absence of myosteatosis. The clinical outcomes, including disease-free survival (DFS), were compared to determine the prognostic significance of myosteatosis. Results: Of 170 patients, 36 (21.2%) were diagnosed with myosteatosis. These patients were older and had a higher body mass index. The myosteatosis group had a higher proportion of females than the no myosteatosis group. Lymphovascular invasion and tumor necrosis were prevalent pathological features in patients with myosteatosis. Kaplan-Meier analysis demonstrated that myosteatosis was associated with significantly shorter DFS (p<0.05). Multivariate analysis confirmed that myosteatosis independently predicted adverse outcomes in patients with localized RCC. Conclusion AI-based CT analysis of myosteatosis is a reliable method for improving the risk stratification of patients with localized RCC. Patients with myosteatosis demonstrate poor pathological features and shorter DFS. These findings highlight the potential of AI-driven body composition analysis to refine prognostic models and personalized treatment strategies.

Purpose: Myosteatosis, defined as fat infiltration in muscle tissue, has been linked to poor outcomes in various cancers. However, the prognostic impact of myosteatosis on renal cell carcinoma (RCC) remains poorly understood. This study evaluated the predictive value of myosteatosis based on an artificial intelligence (AI)-driven computed tomography (CT) analysis in patients with localized RCC who underwent partial nephrectomy. Materials and Methods: This retrospective study included 170 patients with localized RCC who underwent partial nephrectomy at a single institution between 2011 and 2017. Myosteatosis was assessed on CT scans using an AI-based tool. The patients were categorized into 2 groups according to the presence or absence of myosteatosis. The clinical outcomes, including disease-free survival (DFS), were compared to determine the prognostic significance of myosteatosis. Results: Of 170 patients, 36 (21.2%) were diagnosed with myosteatosis. These patients were older and had a higher body mass index. The myosteatosis group had a higher proportion of females than the no myosteatosis group. Lymphovascular invasion and tumor necrosis were prevalent pathological features in patients with myosteatosis. Kaplan-Meier analysis demonstrated that myosteatosis was associated with significantly shorter DFS (p<0.05). Multivariate analysis confirmed that myosteatosis independently predicted adverse outcomes in patients with localized RCC. Conclusion AI-based CT analysis of myosteatosis is a reliable method for improving the risk stratification of patients with localized RCC. Patients with myosteatosis demonstrate poor pathological features and shorter DFS. These findings highlight the potential of AI-driven body composition analysis to refine prognostic models and personalized treatment strategies.