BACKGROUND: The effect of genistein on different types of cells has been investigated. However, its effect on the nervous system is still unclear. The aim of the present work is to explore the effect of genistein on rat neuroblastoma B35 cells. METHODS: The effect of genistein on the proliferation of B35 cells, its cytotoxicity, the cell-cycle distribution, the ultra-structural changes and the induction of apoptosis were determined using MTT assay, LDH assay, Flow-cytometric analysis, transmission electron microscopy and Hoechst staining, respectively. Furthermore, Real-time quantitative RT-PCR and Western blotting were used to examine the transcriptional and post-translational alterations of the G2/M cell-cycle arrest marker cyclin-dependent kinase inhibitor p21(waf1/cip1) and the apoptosis-related genes after genistein treatment. RESULTS: Genistein significantly inhibits cell survival, slightly elevates the release of lactate dehydrogenase and induced apoptosis in B35 cells. Genistein increased the number of cells at S-phase and induced cells to accumulate at the G2/M phase. These G2/M arrested cells are associated with a marked up-regulation of p21(waf1/cip1) at both the mRNA and protein levels. We observed that genistein up-regulates pro-apoptotic Bax with concurrent down-regulation of the anti-apoptotic Bcl-2 protein. CONCLUSION: These observations suggest that the anticancer effect of genistein on B35 neuroblastoma cells is mediated through multiple cellular pathways including G2/M cell-cycle arrest and the induction of apoptosis.
Animals ; Apoptosis* ; Blotting, Western ; Cell Cycle Checkpoints* ; Cell Cycle* ; Cell Survival ; Down-Regulation ; Genistein* ; L-Lactate Dehydrogenase ; Microscopy, Electron, Transmission ; Nervous System ; Neuroblastoma* ; Phosphotransferases ; Rats* ; RNA, Messenger ; Up-Regulation

Background: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. Methods: We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). Results: Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application’s output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss’ kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss’ kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians’ consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients’ sex and age (P < 0.001 for both). Conclusion Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.