Fulminant hepatic failure (FHF) is often accompanied by a myriad of neurologic complications, which are associated with high morbidity and mortality. Although appropriate neuromonitoring is recommended for early diagnosis and to minimize secondary brain injury, individuals with FHF usually have a high chance of coagulopathy, which limits the ability to use invasive neuromonitoring. Jugular bulb venous oxygen saturation (JvO₂) monitoring is well known as a surrogate direct measures of global brain oxygen use. We report the case of a patient with increased intracranial pressure due to FHF, in which JvO₂ was used for appropriate brain oxygen monitoring.
Brain Edema ; Brain Injuries ; Brain* ; Early Diagnosis ; Hepatic Encephalopathy ; Humans ; Intracranial Pressure ; Jugular Veins ; Liver Failure, Acute* ; Mortality ; Oxygen Consumption ; Oxygen*

Purpose: This study aimed to examine whether first-time mothers’ grit, spousal support, and age can make significant differences in latent means of child-rearing passion, postpartum depression, and happiness. Methods: Data were collected from April 2 to July 16, 2019. Two hundred sixteen first-time mothers of infants and toddlers aged 0–2 years participated in a self-reported questionnaire study in which scales of nurturing passion, postpartum depression, happiness, grit, and spousal support were included. The collected data were analyzed with IBM SPSS ver. 18.0 (IBM Co., Armonk, NY, USA) for descriptive statistics and Pearson correlation analyses. In addition, Mplus (ver. 7.0) was used for the Multiple Indicators Multiple Causes (MIMIC) model approach. Results: The MIMIC model yielded an appropriate fit to the data (χ2=103.74, degrees of freedom=53, comparative fit index=0.96, root mean square error of approximation=0.07, standardized root mean square residual=0.05). The paths from grit and spousal support all had significantly positive beta coefficients (p<0.05) to child-rearing passion (β=0.3 and β=0.26, respectively) and happiness (β=0.22 and β=0.39, respectively), while the corresponding paths to postpartum depression were all negatively significant (β= -0.24 for both). These results suggest that unlike chronological maturity (i.e., age), grittier individuals with stronger spousal support display greater passion for child-rearing, as well as greater happiness. In a similar vein, they suffered less from postpartum depression. Conclusion These results imply that grit can be employed to enhance first-time mothers’ child-rearing passion and happiness as it can also concurrently offset the effects of a negative labor and child-birth experience on first-time mothers’ mental health, e.g., in terms of reducing postpartum depression.

Purpose: This study aimed to examine whether first-time mothers’ grit, spousal support, and age can make significant differences in latent means of child-rearing passion, postpartum depression, and happiness. Methods: Data were collected from April 2 to July 16, 2019. Two hundred sixteen first-time mothers of infants and toddlers aged 0–2 years participated in a self-reported questionnaire study in which scales of nurturing passion, postpartum depression, happiness, grit, and spousal support were included. The collected data were analyzed with IBM SPSS ver. 18.0 (IBM Co., Armonk, NY, USA) for descriptive statistics and Pearson correlation analyses. In addition, Mplus (ver. 7.0) was used for the Multiple Indicators Multiple Causes (MIMIC) model approach. Results: The MIMIC model yielded an appropriate fit to the data (χ2=103.74, degrees of freedom=53, comparative fit index=0.96, root mean square error of approximation=0.07, standardized root mean square residual=0.05). The paths from grit and spousal support all had significantly positive beta coefficients (p<0.05) to child-rearing passion (β=0.3 and β=0.26, respectively) and happiness (β=0.22 and β=0.39, respectively), while the corresponding paths to postpartum depression were all negatively significant (β= -0.24 for both). These results suggest that unlike chronological maturity (i.e., age), grittier individuals with stronger spousal support display greater passion for child-rearing, as well as greater happiness. In a similar vein, they suffered less from postpartum depression. Conclusion These results imply that grit can be employed to enhance first-time mothers’ child-rearing passion and happiness as it can also concurrently offset the effects of a negative labor and child-birth experience on first-time mothers’ mental health, e.g., in terms of reducing postpartum depression.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: To evaluate the association between long-term glycated hemoglobin A (HbA1c) variability and treatment outcomes ofanti-vascular endothelial growth factor (VEGF) injection in diabetic macular edema patients. Methods: The medical records of 49 eyes (38 patients) that received anti-VEGF injection for diabetic macular edema were reviewedretrospectively. Best-corrected visual acuity (BCVA) and central macular thickness (CMT) before injection and at onemonth and six months after injection were analyzed. HbA1c variability (HbA1c coefficient of variation [CV]) was calculated usingthe HbA1c results from the year prior to (before) and the year after injection and compared with clinical results. Results: In the group with a low mean HbA1c level before injection, the group with lower HbA1c CV showed greater reduction inmacular edema one month after injection (low HbA1c CV, 122.4 ± 123.2 μm versus high HbA1c CV, 5.2 ± 37.0 μm, p= 0.027).In the group with high mean HbA1c, there was no significant difference between HbA1c variability and clinical features. In a multivariateanalysis, the factor related to the reduction of macular edema was initial CMT (one month adjusted hazard ratio (aHR)0.5, p< 0.001; six months aHR 0.3, p= 0.023). The factor associated with visual gain was initial visual acuity (LogMAR) (onemonth aHR 0.4, p< 0.001; six months aHR 0.4, p< 0.001). The association between mean HbA1c or HbA1c variability and clinicaloutcome was not significant. Conclusions Unlike initial CMT or BCVA, mean HbA1c and HbA1c variability were not significantly associated with clinical outcomesof anti-VEGF injection in diabetic macular edema patients.

BACKGROUND: Posterior reversible encephalopathy syndrome (PRES) is classically characterized by symmetric vasogenic edema in the parietooccipital areas, but may occur at other sites with varying imaging appearances. CASE REPORT: A 55-year old female with chronic kidney disease (CKD) was admitted to the emergency room, presenting with nausea, vomiting and seizure. The initial blood pressure was 145/90 mmHg. Fluid attenuated inversion recovery demonstrated diffuse vasogenic edema in the bilateral cortical and subcortical white matters involving the frontal lobes. Perfusion magnetic resonance imaging (MRP) showed no hyper- or hypoperfusion at blood pressure levels of 140/50 mmHg. A follow-up magnetic resonance imaging at 3 weeks later demonstrated complete resolution of previous lesions. CONCLUSIONS: Earlier reports have demonstrated that PRES can occur in cases of atypical distributions, and features of imaging findings and normotensive settings. It is important to note that PRES is a dynamic process. As a result, we suggest that MRP must be considered in the appropriate temporal framework, to avoid misinterpretation of the other diseases, especially in CKD patients.
Blood Pressure ; Edema ; Emergency Service, Hospital ; Female ; Follow-Up Studies ; Frontal Lobe ; Humans ; Magnetic Resonance Angiography ; Magnetic Resonance Imaging ; Nausea ; Perfusion Imaging ; Perfusion ; Posterior Leukoencephalopathy Syndrome ; Renal Insufficiency, Chronic ; Seizures ; Vomiting ; White Matter