A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation.

ABSTRACT: Neural-tumor electrophysiology-marked by pathological membrane potentials and ion channel dysregulation-emerges as actionable targets to curb tumor aggression. Yet, how neural-driven bioelectrical crosstalk dynamically regulates tumors within functional circuits remains elusive, demanding tools for real-time interaction decoding. Here, we present a machine learning-driven electrophysiological platform that integrates custom microfluidics with real-time decoding of complex neural-tumor signal dynamics. Our findings show that glioma cells selectively hijack specific subsets of neural signals, reshaping waveform properties and synchronizing their firing events with neural activity. This dynamic interaction plays a critical role in boosting glioma invasiveness, as tumor cells harness neural ac

SUBMITTER: Xu T 

PROVIDER: S-EPMC12780002 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature