Effects of JZTX-V on the wild type Kv4.3 Expressed in HEK293T and Molecular Determinants in the Voltage-sensing Domains of Kv4.3 Interacting with JZTX-V.

JZTX-V is a toxin isolated from the venom of the Chinese spider Chilobrachys jingzhao. Previous studies had shown that JZTX-V could inhibit the transient outward potassium current of Kv4.2 and Kv4.3 expressed in Xenopus oocytes but had no effects on Kv1.2-1.4. However, the underlying action mechanism of JZTX-V on Kv4.3 remains unclear. In our study, JZTX-V could inhibit not only transient outward potassium currents evoked in small-sized DRG neurons but also Kv4.3-encoded currents expressed in HEK293T cells in the concentration and voltage dependence. The half maximal inhibitory concentration of JZTX-V on Kv4.3 was 9.6 ± 1.2 nM. In addition, the time course for JZTX-V inhibition and release of inhibition after washout were 15.8 ± 1.54 s and 58.8 ± 4.35 s. Electrophysiological assays indicated that 25 nM JZTX-V could shift significantly the voltage dependence of steady-state activation and steady-state inactivation to depolarization. Meanwhile, 25 nM JZTX-V decreased markedly the time constant of activation and inactivation but had no effect on the time constant of recovery from inactivation. To study the molecular determinants of Kv4.3, we performed alanine scanning on a conserved motif of Kv4.3 and assayed the affinity between mutants and JZTX-V. The results not only showed that I273, L275, V283, and F287 were molecular determinants in the conserved motif of Kv4.3 for interacting with JZTX-V but also speculated the underlying action mechanism that the hydrophobic interaction and steric effects played key roles in the binding of JZTX-V with Kv4.3. In summary, our studies have laid a scientific theoretical foundation for further research on the interaction mechanism between JZTX-V and Kv4.3.

A heart failure staging model based on machine learning classification algorithms / 中国组织工程研究

BACKGROUND:Early detection and accurate staging diagnosis of heart failure are the basis of good clinical therapy efficacy. Due to lack of simple and effective staging model for the diagnosis of heart failure, it is difficult to diagnose heart failure in clinics, leading to poor control of heart failure. OBJECTIVE:To establish the disease staging model based on Adaboost and SVM for heart failure, and improve the accuracy of diagnosis and staging of heart failure. METHODS:A total of 194 cases were roled into this study, including heart failure patients and healthy physical examination persons. According to the stage standards formulated by American Colege of Cardiology and American Heart Association, specific clinical feature parameters closely related to heart failure were colected and selected. Based on clinical diagnosis results and using Adaboost model and SVM model, we trained the models for heart failure diagnosis and staging, thus obtaining diagnosis model. RESULTS AND CONCLUSION: The parameters included stroke volume, cardiac output, left ventricular ejection fraction, left atrial diameter, left ventricular internal diameter at end-systole, N-terminal pro-brain natriuretic peptide and heart rate variability. As for the Adaboost model, its sensitivity and specificity was 100% and 94.4%, respectively. At the same time the SVM model had good sensitivity and specificity, 86.5% and 89.4% respectively. Adaboost classification model can be accurate in the diagnosis of heart failure symptoms, the accuracy reached 89.36%. On the basis of the diagnosis of heart failure, the SVM classification model is effective in staging the severity of heart failure, staging accuracy for staging B and C was 86.49% and 81.48%, respectively. The findings indicate that, combining Adaboost and SVM machine learning models could provide an accurate diagnosis and staging model for heart failure.