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aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

arXiv:2508.15126v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of high-quality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be submitted, reviewed, and iteratively refined by both human and AI scientists. It also provides API and MCP interfaces that enable seamless integration of heterogeneous human and AI scientists, creating a scalable and extensible ecosystem for autonomous scientific discovery. Through extensive experiments, we demonstrate that aiXiv is a reliable and robust platform that significantly enhances the quality of AI-generated research proposals and papers after iterative revising and reviewing on aiXiv. Our work lays the groundwork for a next-generation open-access ecosystem for AI scientists, accelerating the publication and dissemination of high-quality AI-generated research content. Code: https://github.com/aixiv-org aiXiv: https://aixiv.science

Exploring the role of lipid metabolism genes in gastric cancer prognosis and tumor immune microenvironment

J Int Med Res. 2025 Dec;53(12):3000605251403252. doi: 10.1177/03000605251403252. Epub 2025 Dec 11.

ABSTRACT

BackgroundGastric cancer remains a major global health challenge due to its high mortality rate and complex pathophysiological mechanisms. Emerging evidence highlights that dysregulated lipid metabolism contributes to gastric cancer progression and prognosis, but the associations between lipid metabolism-associated genes, gastric cancer patient survival, and tumor immune microenvironment remodeling are not fully elucidated.MethodsWe analyzed publicly available omics and clinical data, including RNA sequencing data from 371 gastric cancer samples in The Cancer Genome Atlas database and 433 gastric cancer samples in the Gene Expression Omnibus database. We first curated the top 100 lipid metabolism-associated genes based on relevance scores. Then, univariate Cox regression was used to identify genes significantly associated with overall survival. Consensus clustering was applied to these survival-related genes to define gastric cancer molecular subtypes. Copy number variation analysis was performed to assess genomic alterations of these genes in tumor samples. A prognostic risk model was constructed using least absolute shrinkage and selection operator regression and validated via multivariate Cox regression. Immune infiltration analysis using CIBERSORT and ESTIMATE algorithms was conducted to explore associations between lipid metabolism-associated genes and tumor immune microenvironment characteristics.ResultsA total of 3911 differentially expressed genes were identified between gastric cancer and adjacent normal tissues. Among the top 100 lipid metabolism-associated genes, 43 were significantly linked to patient survival, most of which were considered as poor prognostic factors. Copy number variation analysis revealed frequent copy number gains of these genes in tumor samples. Consensus clustering stratified patients into two molecular subtypes (LMAGcluster A and LMAGcluster B), with LMAGcluster A showing significantly worse survival outcomes (median survival: 2.6 years vs. 8.3 years in LMAGcluster B, p < 0.001). LMAGcluster A was also characterized by elevated infiltration of pro-tumor immune cells, such as regulatory T cells and follicular helper T cells. The prognostic model based on 14 key lipid metabolism-associated genes exhibited robust predictive performance, with area under the receiver operating characteristic curve values of 0.702-0.761 in The Cancer Genome Atlas cohort and 0.621-0.638 in the Gene Expression Omnibus cohort for 1-, 3-, and 5-year survival.ConclusionLipid metabolism-associated genes are closely associated with gastric cancer prognosis and tumor immune microenvironment remodeling. The identified gene-based molecular subtypes and prognostic model provide novel insights into gastric cancer progression, and the 14 key genes may serve as potential biomarkers and therapeutic targets.

PMID:41381057 | DOI:10.1177/03000605251403252

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