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: Dipeptidyl peptidase-4 (DPP4) inhibitors are frequently prescribed for patients with type 2 diabetes; however, their cost can pose a significant barrier for those with impaired kidney function. This study aimed to estimate the economic benefits of substituting non-renal dose-adjusted (NRDA) DPP4 inhibitors with renal dose-adjusted (RDA) DPP4 inhibitors in patients with both impaired kidney function and type 2 diabetes. Methods: This retrospective cohort study was conducted from January 1, 2012 to December 31, 2018, using data obtained from common data models of five medical centers in Korea. Model 1 applied the prescription pattern of participants with preserved kidney function to those with impaired kidney function. In contrast, model 2 replaced all NRDA DPP4 inhibitors with RDA DPP4 inhibitors, adjusting the doses of RDA DPP4 inhibitors based on individual kidney function. The primary outcome was the cost difference between the two models. Results: In total, 67,964,996 prescription records were analyzed. NRDA DPP4 inhibitors were more frequently prescribed to patients with impaired kidney function than in those with preserved kidney function (25.7%, 51.3%, 64.3%, and 71.6% in patients with estimated glomerular filtration rates [eGFRs] of ≥60, <60, <45, and <30 mL/min/1.73 m2, respectively). When model 1 was applied, the cost savings per year were 7.6% for eGFR <60 mL/min/1.73 m2 and 30.4% for eGFR <30 mL/min/1.73 m2. According to model 2, 15.4% to 51.2% per year could be saved depending on kidney impairment severity. Conclusion Adjusting the doses of RDA DPP4 inhibitors based on individual kidney function could alleviate the economic burden associated with medical expenses.

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.