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.

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.

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.

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.

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.