Bacillus Calmette-Guérin (BCG) serves as an anticancer drug for bladder cancer by enhancing the innate immune response and facilitating the expression of beta-defensin-2/-3. BCG is significantly more effective than other treatment modalities; however, it has limitations due to the nonspecific secretion of immune proteins such as interleukin-2 (IL-2) and IFN-γ, necessitating frequent injections that result in toxicity. The newly developed BCG-cell wall skeleton (BCG-CWS) is intended to address the non-specificity and the requirement for repeated treatments associated with BCG. BCG-CWS stimulates antigen-presenting cells by secreting cytokines such as IL-12, using an adjuvant to enhance the immune response and synergize with it to provoke a potent immune reaction. Nevertheless, BCG-CWS encounters issues related to cellular uptake due to the substantial molecular weight of the drug.To meet this challenge, various strategies such as the introduction of R8 protein, the liposome evaporated via an emulsified lipid method, and nanoparticle formulation have been employed which can enhance targeted drug delivery, though issues related to particle size remain unresolved. This paper aims to discuss future perspectives by examining the mechanisms and challenges of BCG-CWS.

Bacillus Calmette-Guérin (BCG) serves as an anticancer drug for bladder cancer by enhancing the innate immune response and facilitating the expression of beta-defensin-2/-3. BCG is significantly more effective than other treatment modalities; however, it has limitations due to the nonspecific secretion of immune proteins such as interleukin-2 (IL-2) and IFN-γ, necessitating frequent injections that result in toxicity. The newly developed BCG-cell wall skeleton (BCG-CWS) is intended to address the non-specificity and the requirement for repeated treatments associated with BCG. BCG-CWS stimulates antigen-presenting cells by secreting cytokines such as IL-12, using an adjuvant to enhance the immune response and synergize with it to provoke a potent immune reaction. Nevertheless, BCG-CWS encounters issues related to cellular uptake due to the substantial molecular weight of the drug.To meet this challenge, various strategies such as the introduction of R8 protein, the liposome evaporated via an emulsified lipid method, and nanoparticle formulation have been employed which can enhance targeted drug delivery, though issues related to particle size remain unresolved. This paper aims to discuss future perspectives by examining the mechanisms and challenges of BCG-CWS.

Bacillus Calmette-Guérin (BCG) serves as an anticancer drug for bladder cancer by enhancing the innate immune response and facilitating the expression of beta-defensin-2/-3. BCG is significantly more effective than other treatment modalities; however, it has limitations due to the nonspecific secretion of immune proteins such as interleukin-2 (IL-2) and IFN-γ, necessitating frequent injections that result in toxicity. The newly developed BCG-cell wall skeleton (BCG-CWS) is intended to address the non-specificity and the requirement for repeated treatments associated with BCG. BCG-CWS stimulates antigen-presenting cells by secreting cytokines such as IL-12, using an adjuvant to enhance the immune response and synergize with it to provoke a potent immune reaction. Nevertheless, BCG-CWS encounters issues related to cellular uptake due to the substantial molecular weight of the drug.To meet this challenge, various strategies such as the introduction of R8 protein, the liposome evaporated via an emulsified lipid method, and nanoparticle formulation have been employed which can enhance targeted drug delivery, though issues related to particle size remain unresolved. This paper aims to discuss future perspectives by examining the mechanisms and challenges of BCG-CWS.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.