Objectives: Some reports suggest that the concentration-to-dosage ratio (C/D ratio) of clozapine (CZP) in Asian treatment-resistant schizophrenia (TRS) patients differs from that of Caucasian TRS patients. However, there is insufficient research on the differences in C/D ratio between Korean TRS patients and Caucasian TRS patients. Therefore, this study aimed to investigate prescribed CZP dosage, CZP concentration and C/D ratio in Korean TRS patients. Methods: The study included TRS patients aged 18 years or older who were prescribed CZP for at least 12 weeks at a psychiatric hospital in Korea. We collected demographic information, smoking status, hospitalization status, CZP serum concentration, total CZP dosage, and norclozapine (NCZP) serum concentration and analyzed their statistical correlations. Results: The study found that the average daily CZP dosage was 266.1 mg, and the average CZP concentration was 568.0 ng/mL. There was a significant correlation between CZP serum concentration and smoking status, as well as sex. CZP dosage was not significantly associated with age, weight, BMI, or metabolic rate. The study also found a significant difference in C/D ratio between groups based on CZP serum concentration. Conclusion Our study suggests that recommended CZP dosages for Caucasians may not be suitable for Koreans due to C/D ratio differences. We found a relationship between CZP serum concentration and C/D ratio in Korean TRS patients. Therefore, it is crucial to confirm CZP serum concentration to avoid side effects and to find optimal dosage.

The lung is a complex organ comprising a branched airway that connects the large airway and millions of terminal gas-exchange units. Traditional pulmonary biomedical research by using cell line model system have limitations such as lack of cellular heterogeneity, animal models also have limitations including ethical concern, race-to-race variations, and physiological differences found in vivo. Organoids and on-a-chip models offer viable solutions for these issues.Organoids are three-dimensional, self-organized construct composed of numerous cells derived from stem cells cultured with growth factors required for the maintenance of stem cells. On-a-chip models are biomimetic microsystems which are able to customize to use microfluidic systems to simulate blood flow in blood channels or vacuum to simulate human breathing. This review summarizes the key components and previous biomedical studies conducted on lung organoids and lung-on-a-chip models, and introduces potential future applications. Considering the importance and benefits of these model systems, we believe that the system will offer better platform to biomedical researchers on pulmonary diseases, such as emerging viral infection, progressive fibrotic pulmonary diseases, or primary or metastatic lung cancer.

Purpose: Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set. Materials and Methods: The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores. Results: The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002). Conclusion Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy.