Pictilisib

Objective. Spontaneous network bursts (NBs) are critical for neuronal circuit development, influencing synaptogenesis and functional organization. While JNK and PI3K-Akt signaling pathways are known to regulate synaptic plasticity, their specific roles in governing NBs dynamics and functional network organization remain poorly understood. This study investigates the roles of JNK and PI3K-Akt signaling in regulating spontaneous NBs dynamics and network organization in cultured neuronal networks. Approach. Using longitudinal microelectrode array (MEA) recordings from cultured cortical neurons (DIV14-49), we pharmacologically inhibited JNK (SP600125, JNK-IN-8) and PI3K-Akt (LY294002, GDC-0941) pathways. We quantitatively analyzed NBs profiles (maximum firing rate/MFR, burst length/BL, rising phase/RP) and functional network properties (modularity, betweenness centrality) during development. Main results. JNK inhibition increased MFR but reduced RP and FP, and decreased betweenness centrality and network modularity, particularly in DIV21. PI3K-Akt inhibition caused delayed effects: decreased MFR at DIV49 with increased RP, while enhancing network modularity. Developmental analysis revealed a transition from core-node-driven NBs (strong MFR-betweenness and BL-betweenness correlation at DIV14) to modularly organized NBs (strong BL-modularity and MFR-modularity correlation at DIV49), with pathway inhibitors differentially altering these relationships. Significance. Our findings demonstrate that JNK and PI3K-Akt pathways play distinct temporal roles in regulating NBs dynamics and network organization. JNK signaling is crucial for maintaining early core-node functionality, whereas PI3K-Akt signaling promotes the development of mature modular architecture. Our findings enhance the understanding of how molecular signaling influences neuronal network dynamics, contributing to a broader framework for studying neurodevelopmental principles.

Predicting the patient's response to anticancer drugs is essential in personalized treatment plans. However, due to significant distribution differences between cell line data and patient data, models trained well on cell line data may perform poorly on patient anticancer drug response predictions. Some existing methods use transfer learning strategies to implement domain feature alignment between cell lines and patient data and leverage knowledge from cell lines to predict patient anticancer drug responses. This study proposes a domain alignment method based on masked variational autoencoders, MVAEDA, to predict patient anticancer drug responses. The model constructs multiple variational autoencoders (VAEs) and mask predictors to extract specific and domain-invariant features of cell lines and patients. Then, it masks and reconstructs the gene expression matrix, using generative adversarial training to learn domain-invariant features from the cell line and patient domains. These domain-invariant features are then used to train a classifier. Finally, the final trained model predicts the anticancer drug response in the target domain. Our model is experimentally evaluated on the clinical dataset and the preclinical dataset. The results show that our method performs better than other state-of-the-art methods.