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Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies

By: Yankai Chen · Xinni Zhang · Yifei Zhang · Yangning Li · Henry Peng Zou · Chunyu Miao · Weizhi Zhang · Xue Liu · Philip S. Yu

28 October 2025 at 12:00

arXiv:2510.22095v1 Announce Type: new Abstract: Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurological impairments. However, their widespread adoption is hindered by critical limitations, such as low information transfer rates and extensive user-specific calibration. To overcome these challenges, recent research has explored the integration of Large Language Models (LLMs), extending the focus from simple command decoding to understanding complex cognitive states. Despite these advancements, deploying agentic AI faces technical hurdles and ethical concerns. Due to the lack of comprehensive discussion on this emerging direction, this position paper argues that the field is poised for a paradigm extension from BCI to Brain-Agent Collaboration (BAC). We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors, demanding a focus on ethical data handling, model reliability, and a robust human-agent collaboration framework to ensure these systems are safe, trustworthy, and effective.

Identification of C4BPA as a genetically informed drug target in NSCLC: an integrative single-cell and multi-omics study based on the druggable genes

Omics In Lung

By: Zhihan Xiao · Xinji Liu · Wei Tang · Yan Lv · Tongyu Zhang · Xu Zhan · Qihang Sun · Willis Wasonga Omindo · Qi Wang · Ruijie Zhang · Wei Ping · Ni Zhang

7 October 2025 at 18:00

Hum Genomics. 2025 Oct 6;19(1):113. doi: 10.1186/s40246-025-00829-3.

ABSTRACT

BACKGROUND: Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. Despite advancements in treatment, drug resistance and limited therapeutic efficacy persist, underscoring the urgent need for novel and mechanistically informed therapeutic strategies. Identifying genetically supported drug targets may accelerate the development of precision therapies in NSCLC.

METHODS: We implemented an integrative multi-omics framework combining single-cell RNA sequencing (scRNA-seq), genome-wide association studies (GWAS), and molecular quantitative trait locus (QTL) datasets including expression (eQTL), protein (pQTL), and DNA methylation (mQTL) QTLs. Druggable candidates were systematically evaluated using a suite of Mendelian randomization (MR) approaches-including summary data-based MR (SMR), generalized SMR (GSMR), and genetic risk score (GRS) analysis. Epigenetic regulation and downstream signaling were further explored through mediation MR analysis.

RESULTS: C4BPA, a complement-regulatory macromolecule, emerged as a risk factor for NSCLC across multiple MR models, with consistent findings validated at both transcriptomic and proteomic levels. Epigenetic activation of C4BPA via DNA methylation was observed, and C4BPA expression was shown to promote NSCLC progression through the inflammatory chemokine CCL8 signaling axis. Sensitivity analyses confirmed the robustness of association inference.

CONCLUSIONS: Our findings identify C4BPA as a genetically validated and biologically plausible therapeutic target for NSCLC. This study demonstrates the power of integrating single-cell transcriptomics with population-scale omics and association inference to uncover actionable targets, offering a scalable framework for advancing precision oncology in lung cancer.

PMID:41053817 | PMC:PMC12502296 | DOI:10.1186/s40246-025-00829-3