Na Li

This study engineers segmented poly(A) tails with microRNA target sites to enable tissue-specific regulation of mRNA-LNPs, showing position-dependent expression control in vitro and in vivo.

The paper uses single-cell foundation models to predict durable CAR T response from pre-infusion scRNA-seq data, achieving high accuracy without requiring perfect cell annotation.

DeviceAgent is an AI agent that integrates LLMs and VLMs to automate bioelectronics research tasks, demonstrating capabilities in design, fabrication, defect inspection, and electrophysiological analysis.

This paper introduces BCI-Agent, an AI framework that classifies neuronal cell types from electrophysiological data without fine-tuning, validated in rodent and human datasets.

BehaveAgent automates cross-species behavior analysis from video input using multimodal AI without manual intervention or retraining, enabling zero-shot reasoning and end-to-end analysis.

SpikeAgent automates spike sorting with a multimodal LLM-based AI agent, achieving curation consistency equal to or exceeding human experts and drastically reducing expertise barriers.

This paper introduces an AI-cyborg system that uses adaptive intelligent modulation to enhance cardiac organoid maturation through real-time feedback.

This paper introduces UnitedNet, a multi-task deep learning model for analyzing single-cell multi-modality data, showing improved performance in joint group identification and cross-modal prediction.