Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a common cause of autoimmune encephalitis. The condition is difficult to diagnose or suspect in the emergency department because it usually presents with nonspecific neurological or psychiatric symptoms. It is often mistaken for viral encephalitis or psychiatric illness. This paper reports a case of anti-NMDAR encephalitis in which the patient experienced mood changes, including suicidal ideation, which led to a delayed diagnosis after three visits to the emergency department. This paper aims to raise awareness among emergency physicians about the possibility of anti-NMDAR encephalitis and to encourage them to consider it in their differential diagnosis in the emergency department.

Purpose: The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy. Methods: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses. Results: Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605–0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8–22.2), and 14.8 months (95% CI, 14.2–15.4), respectively (P = 0.431, log-rank test). Conclusion Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.

Objective: Many studies have examined the July effect. However, little is known about the July effect in sepsis. We hypothesized that the July effect would result in worse outcomes for patients with sepsis. Methods: Data from patients with sepsis, collected prospectively between January 2018 and December 2021, were analyzed. In Korea, the new academic year starts on March 1, so the “July effect” appears in March. The primary outcome was 30-day mortality. Secondary outcomes included adherence to the Surviving Sepsis Campaign bundle. Outcomes in March were compared to other months. A multivariate Cox proportional hazard regression was performed to adjust for confounders. Results: We included 843 patients. There were no significant differences in sepsis severity. The 30-day mortality in March was higher (49.0% vs. 28.5%, P<0.001). However, there was no difference in bundle adherence in March (42.2% vs. 48.0%, P=0.264). The multivariate Cox proportional hazard regression showed that the July effect was associated with 30-day mortality in patients with sepsis (adjusted hazard ratio, 1.925; 95% confidence interval, 1.405–2.638; P<0.001). Conclusion The July effect was associated with 30-day mortality in patients with sepsis. However, bundle adherence did not differ. These results suggest that the increase in mortality during the turnover period might be related to unmeasured in-hospital management. Intensive supervision and education of residents caring for patients with sepsis is needed in the beginning of training.

Objective: Many studies have examined the July effect. However, little is known about the July effect in sepsis. We hypothesized that the July effect would result in worse outcomes for patients with sepsis. Methods: Data from patients with sepsis, collected prospectively between January 2018 and December 2021, were analyzed. In Korea, the new academic year starts on March 1, so the “July effect” appears in March. The primary outcome was 30-day mortality. Secondary outcomes included adherence to the Surviving Sepsis Campaign bundle. Outcomes in March were compared to other months. A multivariate Cox proportional hazard regression was performed to adjust for confounders. Results: We included 843 patients. There were no significant differences in sepsis severity. The 30-day mortality in March was higher (49.0% vs. 28.5%, P<0.001). However, there was no difference in bundle adherence in March (42.2% vs. 48.0%, P=0.264). The multivariate Cox proportional hazard regression showed that the July effect was associated with 30-day mortality in patients with sepsis (adjusted hazard ratio, 1.925; 95% confidence interval, 1.405–2.638; P<0.001). Conclusion The July effect was associated with 30-day mortality in patients with sepsis. However, bundle adherence did not differ. These results suggest that the increase in mortality during the turnover period might be related to unmeasured in-hospital management. Intensive supervision and education of residents caring for patients with sepsis is needed in the beginning of training.

Objective: Many studies have examined the July effect. However, little is known about the July effect in sepsis. We hypothesized that the July effect would result in worse outcomes for patients with sepsis. Methods: Data from patients with sepsis, collected prospectively between January 2018 and December 2021, were analyzed. In Korea, the new academic year starts on March 1, so the “July effect” appears in March. The primary outcome was 30-day mortality. Secondary outcomes included adherence to the Surviving Sepsis Campaign bundle. Outcomes in March were compared to other months. A multivariate Cox proportional hazard regression was performed to adjust for confounders. Results: We included 843 patients. There were no significant differences in sepsis severity. The 30-day mortality in March was higher (49.0% vs. 28.5%, P<0.001). However, there was no difference in bundle adherence in March (42.2% vs. 48.0%, P=0.264). The multivariate Cox proportional hazard regression showed that the July effect was associated with 30-day mortality in patients with sepsis (adjusted hazard ratio, 1.925; 95% confidence interval, 1.405–2.638; P<0.001). Conclusion The July effect was associated with 30-day mortality in patients with sepsis. However, bundle adherence did not differ. These results suggest that the increase in mortality during the turnover period might be related to unmeasured in-hospital management. Intensive supervision and education of residents caring for patients with sepsis is needed in the beginning of training.

Objective: Many studies have examined the July effect. However, little is known about the July effect in sepsis. We hypothesized that the July effect would result in worse outcomes for patients with sepsis. Methods: Data from patients with sepsis, collected prospectively between January 2018 and December 2021, were analyzed. In Korea, the new academic year starts on March 1, so the “July effect” appears in March. The primary outcome was 30-day mortality. Secondary outcomes included adherence to the Surviving Sepsis Campaign bundle. Outcomes in March were compared to other months. A multivariate Cox proportional hazard regression was performed to adjust for confounders. Results: We included 843 patients. There were no significant differences in sepsis severity. The 30-day mortality in March was higher (49.0% vs. 28.5%, P<0.001). However, there was no difference in bundle adherence in March (42.2% vs. 48.0%, P=0.264). The multivariate Cox proportional hazard regression showed that the July effect was associated with 30-day mortality in patients with sepsis (adjusted hazard ratio, 1.925; 95% confidence interval, 1.405–2.638; P<0.001). Conclusion The July effect was associated with 30-day mortality in patients with sepsis. However, bundle adherence did not differ. These results suggest that the increase in mortality during the turnover period might be related to unmeasured in-hospital management. Intensive supervision and education of residents caring for patients with sepsis is needed in the beginning of training.

We report a case of ruptured mycotic aneurysm involving innominate artery requiring an urgent surgical treatment. A 62-yr-old woman presented with fever and dyspnea. Previously, she was diagnosed with colon cancer and received right hemicolectomy and one cycle of adjuvant chemotherapy. On echocardiogram, pericardial effusion was noted and emergency pericardiocentesis was performed. CT scan revealed aortic aneurysm involving ascending aorta and innominate artery, and thrombi surrounding those structures. Patch repair of the defect in the ascending aorta and ringed Goretex graft to bypass the innominate and ascending aorta were performed. We believe that this is the first case of ruptured mycotic aneurysm involving innominate artery.
Aneurysm, Infected/*surgery ; Aorta/pathology ; Aortic Aneurysm/surgery ; Brachiocephalic Trunk/pathology ; Colonic Neoplasms/drug therapy/surgery ; Female ; Human ; Middle Aged ; Tomography, X-Ray Computed ; Transplants

The natural flavonoid macakurzin C (1) exhibited adiponectin biosynthesis-inducing activity during adipogenesis in human bone marrow mesenchymal stem cells and its molecular mechanism was directly associated with a pan-peroxisome proliferator-activated receptor (PPAR) modulator affecting all three PPAR subtypes α, γ, and δ. In this study, increases in adiponectin biosynthesisinducing activity by macakurzin C derivatives (2–7) were studied. The most potent adiponectin biosynthesis-inducing compound 6, macakurzin C 3,5-dimethylether, was elucidated as a dual PPARα/γ modulator. Compound 6 may exhibit the most potent activity because of the antagonistic relationship between PPARδ and PPARγ. Docking studies revealed that the O-methylation of macakurzin C to generate compound 6 significantly disrupted PPARδ binding. Compound 6 has therapeutic potential in hypoadiponectinemia-related metabolic diseases.

BACKGROUND: Dihydrotestosterone (DHT) induces androgenic alopecia by shortening the hair follicle growth phase, resulting in hair loss. We previously demonstrated how changes in the microRNA (miRNA) expression profile influenced DHT-mediated cell death, cell cycle arrest, cell viability, the generation of reactive oxygen species (ROS), and senescence. Protective effects against DHT have not, however, been elucidated at the genome level. OBJECTIVE: We showed that epigallocatechin gallate (EGCG), a major component of green tea, protects DHT-induced cell death by regulating the cellular miRNA expression profile. METHODS: We used a miRNA microarray to identify miRNA expression levels in human dermal papilla cells (DPCs). We investigated whether the miRNA expression influenced the protective effects of EGCG against DHT-induced cell death, growth arrest, intracellular ROS levels, and senescence. RESULTS: EGCG protected against the effects of DHT by altering the miRNA expression profile in human DPCs. In addition, EGCG attenuated DHT-mediated cell death and growth arrest and decreased intracellular ROS levels and senescence. A bioinformatics analysis elucidated the relationship between the altered miRNA expression and EGCG-mediated protective effects against DHT. CONCLUSION: Overall, our results suggest that EGCG ameliorates the negative effects of DHT by altering the miRNA expression profile in human DPCs.
Aging ; Alopecia ; Cell Cycle Checkpoints ; Cell Death ; Cell Survival ; Computational Biology ; Dihydrotestosterone ; Genome ; Hair ; Hair Follicle ; Humans* ; MicroRNAs* ; Reactive Oxygen Species ; Tea

Adiponectin secretion-promoting compounds have therapeutic potentials in human metabolic diseases. Diallyl biphenyl-type neolignan compounds, magnolol, honokiol, and 4-O-methylhonokiol, from a Magnolia officinalis extract were screened as adiponectin-secretion promoting compounds in the adipogenic differentiation model of human bone marrow mesenchymal stem cells (hBM-MSCs). In a target identification study, magnolol, honokiol, and 4-O-methylhonokiol were elucidated as PPARα and PPARγ dual modulators. Diallyl biphenyl-type neolignans affected the transcription of lipid metabolism-associated genes in a different way compared to those of specific PPAR ligands. The diallyl biphenyl-type neolignan structure provides a novel pharmacophore of PPARα/γ dual modulators, which may have unique therapeutic potentials in diverse metabolic diseases.