Bone defects are common disease requiring thorough treatments since the bone is a complex vascularized tissue that is composed of multiple cell types embedded within an intricate extracellular matrix (ECM). For past decades, tissue engineering using cells, proteins, and scaffolds has been suggested as one of the promising approaches for effective bone regeneration. Recently, many researchers have been interested in designing effective platform for tissue regeneration by orchestrating factors involved in microenvironment around tissues. Among factors affecting bone formation, vascularization during bone development and after minor insults via endochondral and intramembranous ossification is especially critical for the long-term support for functional bone. In order to create vascularized bone constructs, the interactions between human mesenchymal stem cells (MSCs) and endothelial cells (ECs) have been investigated using both direct and indirect co-culture studies. Recently, various culture methods including micropatterning techniques, three dimensional scaffolds, and microfluidics have been developed to create micro-engineered platforms that mimic the nature of vascularized bone formation, leading to the creation of functional bone structures. This review focuses on MSCs co-cultured with endothelial cells and micro-engineered platforms to determine the underlying interplay between co-cultured MSCs and vascularized bone constructs, which is ultimately necessary for adequate regeneration of bone defects.
Bone and Bones* ; Bone Development ; Bone Regeneration ; Coculture Techniques ; Endothelial Cells ; Extracellular Matrix ; Humans ; Mesenchymal Stromal Cells* ; Microfluidics ; Osteogenesis ; Regeneration ; Stem Cells ; Tissue Engineering

BACKGROUND/AIMS: Chronic liver disease is a major widespread cause of death, and whole liver transplantation is the only definitive treatment for patients with end-stage liver diseases. However, many problems, including donor shortage, surgical complications and cost, hinder their usage. Recently, tissue-engineering technology provided a potential breakthrough for solving these problems. Three-dimensional (3D) printing technology has been used to mimic tissues and organs suitable for transplantation, but applications for the liver have been rare. METHODS: A 3D bioprinting system was used to construct 3D printed hepatic structures using alginate. HepG2 cells were cultured on these 3D structures for 3 weeks and examined by fluorescence microscopy, histology and immunohistochemistry. The expression of liver-specific markers was quantified on days 1, 7, 14, and 21. RESULTS: The cells grew well on the alginate scaffold, and liver-specific gene expression increased. The cells grew more extensively in 3D culture than two-dimensional culture and exhibited better structural aspects of the liver, indicating that the 3D bioprinting method recapitulates the liver architecture. CONCLUSIONS: The 3D bioprinting of hepatic structures appears feasible. This technology may become a major tool and provide a bridge between basic science and the clinical challenges for regenerative medicine of the liver.
Bioprinting ; Cause of Death ; Gene Expression ; Hep G2 Cells* ; Humans ; Immunohistochemistry ; Liver ; Liver Diseases ; Liver Transplantation ; Methods ; Microscopy, Fluorescence ; Printing, Three-Dimensional ; Regenerative Medicine ; Tissue Donors

National cohort data collected during the coronavirus disease 2019 (COVID-19) delta and omicron periods in Korea revealed a lower risk of severe infection in recipients of three doses of the COVID-19 vaccine (adjusted odds ratio [aOR], 0.05–0.08). The risk of death was reduced during the omicron period compared to the delta period (aOR, 0.75; 95% confidence interval, 0.67–0.84).

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

In November 2021, 14 international travel-related severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 (omicron) variant of concern (VOC) patients were detected in South Korea. Epidemiologic investigation revealed community transmission of the omicron VOC. A total of 80 SARS-CoV-2 omicron VOC-positive patients were identified until December 10, 2021 and 66 of them reported no relation to the international travel.There may be more transmissions with this VOC in Korea than reported.