Purpose: Regenerative medicine is expected to offer an alternative to liver transplantation for treating liver diseases in the future, with one significant challenge being the establishment of an effective stem cell administration route. This study assessed the antifibrogenic effects of adipose-derived stem cells (ASCs) in a liver fibrosis mouse model, focusing on 2 methods of delivery: intravenous injection and scaffold implantation. Methods: An extracellular matrix mimic scaffold was utilized for culturing peroxisome proliferator-activated receptor gamma coactivator 1-alpha–overexpressing ASCs (tASCs). These scaffolds, laden with tASCs, were then implanted subcutaneously in mice exhibiting liver fibrosis. In contrast, the Cell groups received biweekly intravenous injections of tASCs for 4 weeks. After 4 weeks, tissue samples were harvested from the euthanized mice for subsequent analysis. Results: Real-time PCR and Western blot analyses on liver tissues, focusing on markers like alpha-smooth muscle actin (α-SMA), matrix metalloproteinase-2, and transforming growth factor-beta 1 (TGF-β1), showed that both delivery routes substantially lowered fibrotic and inflammatory markers compared to controls (P < 0.05), with no significant differences between the routes. Histological examinations, along with immunohistochemical analysis of α-SMA, collagen type I alpha, and TGF-β1, revealed that the scaffold implantation approach resulted in a greater reduction in fibrosis and lower immunoreactivity for fibrotic markers than intravenous delivery (P < 0.05). Conclusion These findings indicate that delivering tASCs via a scaffold could be more effective, or at least similarly effective, in treating liver fibrosis compared to intravenous delivery. Scaffold implantation could offer a beneficial alternative to frequent intravenous treatments, suggesting its potential utility in clinical applications for liver disease treatment.

Purpose: Regenerative medicine is expected to offer an alternative to liver transplantation for treating liver diseases in the future, with one significant challenge being the establishment of an effective stem cell administration route. This study assessed the antifibrogenic effects of adipose-derived stem cells (ASCs) in a liver fibrosis mouse model, focusing on 2 methods of delivery: intravenous injection and scaffold implantation. Methods: An extracellular matrix mimic scaffold was utilized for culturing peroxisome proliferator-activated receptor gamma coactivator 1-alpha–overexpressing ASCs (tASCs). These scaffolds, laden with tASCs, were then implanted subcutaneously in mice exhibiting liver fibrosis. In contrast, the Cell groups received biweekly intravenous injections of tASCs for 4 weeks. After 4 weeks, tissue samples were harvested from the euthanized mice for subsequent analysis. Results: Real-time PCR and Western blot analyses on liver tissues, focusing on markers like alpha-smooth muscle actin (α-SMA), matrix metalloproteinase-2, and transforming growth factor-beta 1 (TGF-β1), showed that both delivery routes substantially lowered fibrotic and inflammatory markers compared to controls (P < 0.05), with no significant differences between the routes. Histological examinations, along with immunohistochemical analysis of α-SMA, collagen type I alpha, and TGF-β1, revealed that the scaffold implantation approach resulted in a greater reduction in fibrosis and lower immunoreactivity for fibrotic markers than intravenous delivery (P < 0.05). Conclusion These findings indicate that delivering tASCs via a scaffold could be more effective, or at least similarly effective, in treating liver fibrosis compared to intravenous delivery. Scaffold implantation could offer a beneficial alternative to frequent intravenous treatments, suggesting its potential utility in clinical applications for liver disease treatment.

Purpose: Regenerative medicine is expected to offer an alternative to liver transplantation for treating liver diseases in the future, with one significant challenge being the establishment of an effective stem cell administration route. This study assessed the antifibrogenic effects of adipose-derived stem cells (ASCs) in a liver fibrosis mouse model, focusing on 2 methods of delivery: intravenous injection and scaffold implantation. Methods: An extracellular matrix mimic scaffold was utilized for culturing peroxisome proliferator-activated receptor gamma coactivator 1-alpha–overexpressing ASCs (tASCs). These scaffolds, laden with tASCs, were then implanted subcutaneously in mice exhibiting liver fibrosis. In contrast, the Cell groups received biweekly intravenous injections of tASCs for 4 weeks. After 4 weeks, tissue samples were harvested from the euthanized mice for subsequent analysis. Results: Real-time PCR and Western blot analyses on liver tissues, focusing on markers like alpha-smooth muscle actin (α-SMA), matrix metalloproteinase-2, and transforming growth factor-beta 1 (TGF-β1), showed that both delivery routes substantially lowered fibrotic and inflammatory markers compared to controls (P < 0.05), with no significant differences between the routes. Histological examinations, along with immunohistochemical analysis of α-SMA, collagen type I alpha, and TGF-β1, revealed that the scaffold implantation approach resulted in a greater reduction in fibrosis and lower immunoreactivity for fibrotic markers than intravenous delivery (P < 0.05). Conclusion These findings indicate that delivering tASCs via a scaffold could be more effective, or at least similarly effective, in treating liver fibrosis compared to intravenous delivery. Scaffold implantation could offer a beneficial alternative to frequent intravenous treatments, suggesting its potential utility in clinical applications for liver disease treatment.

Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Purpose: The Korean Society of Pediatric Neuro-Oncology (KSPNO) conducted treatment strategies for children with medulloblastoma (MB) by using alkylating agents for maintenance chemotherapy or tandem high-dose chemotherapy (HDC) with autologous stem cell rescue (ASCR) according to the risk stratification. The purpose of the study was to assess treatment outcomes and complications based on risk-adapted treatment and HDC. Materials and Methods: Fifty-nine patients diagnosed with MB were enrolled in this study. Patients in the standard-risk (SR) group received radiotherapy (RT) after surgery and chemotherapy using the KSPNO M051 regimen. Patients in the high-risk (HR) group received two and four chemotherapy cycles according to the KSPNO S081 protocol before and after reduced RT for age following surgery and two cycles of tandem HDC with ASCR consolidation treatment. Results: In the SR group, 24 patients showed 5-year event-free survival (EFS) and overall survival (OS) estimates of 86.7% (95% confidence interval [CI], 73.6 to 100) and 95.8% (95% CI, 88.2 to 100), respectively. In the HR group, more infectious complications and mortality occurred during the second HDC than during the first. In the HR group, the 5-year EFS and OS estimates were 65.5% (95% CI, 51.4 to 83.4) and 72.3% (95% CI, 58.4 to 89.6), respectively. Conclusion High intensity of alkylating agents for SR resulted in similar outcomes but with a high incidence of hematologic toxicity. Tandem HDC with ASCR for HR induced favorable EFS and OS estimates compared to those reported previously. However, infectious complications and treatment-related mortalities suggest that a reduced chemotherapy dose is necessary, especially for the second HDC.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

The objective of standard guideline for utilization of human lung organoids is to provide the basic guidelines required for the manufacture, culture, and quality control of the lung organoids for use in non-clinical efficacy and inhalation toxicity assessments of the respiratory system. As a first step towards the utilization of human lung organoids, the current guideline provides basic, minimal standards that can promote development of alternative testing methods, and can be referenced not only for research, clinical, or commercial uses, but also by experts and researchers at regulatory institutions when assessing safety and efficacy.