cs.AI, q-bio.NC updates on arXiv.org
UniMark: Artificial Intelligence Generated Content Identification Toolkit Meilin Li · Ji He · Jia Xu · Shanzhe Lei · Yan Teng · Yingchun Wang · Xuhong Wang
arXiv:2512.12324v1 Announce Type: cross Abstract: The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \textbf{UniMark}, an open-source, unified framework for multimodal content governance. Our system features a modular unified engine that abstracts complexi
16 December 2025 at 13:00

UniMark: Artificial Intelligence Generated Content Identification Toolkit

By: Meilin Li · Ji He · Jia Xu · Shanzhe Lei · Yan Teng · Yingchun Wang · Xuhong Wang

16 December 2025 at 13:00

arXiv:2512.12324v1 Announce Type: cross Abstract: The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \textbf{UniMark}, an open-source, unified framework for multimodal content governance. Our system features a modular unified engine that abstracts complexities across text, image, audio, and video modalities. Crucially, we propose a novel dual-operation strategy, natively supporting both \emph{Hidden Watermarking} for copyright protection and \emph{Visible Marking} for regulatory compliance. Furthermore, we establish a standardized evaluation framework with three specialized benchmarks (Image/Video/Audio-Bench) to ensure rigorous performance assessment. This toolkit bridges the gap between advanced algorithms and engineering implementation, fostering a more transparent and secure digital ecosystem.

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
Causal relationship of immune cell characteristics in hepatocellular carcinoma: A multi-omics analysis based on Mendelian randomization XiaoLing Tian · BaoChun Wang · XinYi Zhang · Ge Song · YuBo Liu · YuQian Gao · JinYan Wang · YunQi Hua
Medicine (Baltimore). 2025 Dec 5;104(49):e45942. doi: 10.1097/MD.0000000000045942.ABSTRACTThe tumor immune microenvironment of hepatocellular carcinoma (HCC) is complex, yet the causal relationship between immune cell subpopulations and HCC risk remains incompletely elucidated. This study aims to systematically evaluate the causal association between immune cell subpopulations and HCC using Mendelian randomization (MR) analysis, and to validate the biological mechanisms underlying these associat
10 December 2025 at 19:00

Causal relationship of immune cell characteristics in hepatocellular carcinoma: A multi-omics analysis based on Mendelian randomization

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: XiaoLing Tian · BaoChun Wang · XinYi Zhang · Ge Song · YuBo Liu · YuQian Gao · JinYan Wang · YunQi Hua

10 December 2025 at 19:00

Medicine (Baltimore). 2025 Dec 5;104(49):e45942. doi: 10.1097/MD.0000000000045942.

ABSTRACT

The tumor immune microenvironment of hepatocellular carcinoma (HCC) is complex, yet the causal relationship between immune cell subpopulations and HCC risk remains incompletely elucidated. This study aims to systematically evaluate the causal association between immune cell subpopulations and HCC using Mendelian randomization (MR) analysis, and to validate the biological mechanisms underlying these associations through multi-omics data. Bidirectional two-sample MR analysis was performed to examine causal relationships between 731 immune cell subpopulations and HCC. Inverse-variance weighting (IVW) served as the primary analysis method, with robustness validation through Bayesian weighted MR (BWMR) and machine learning algorithms. Therefore, for significantly associated immune subpopulations, independent analyses of gene expression, prognosis, and tumor immune microenvironment were conducted using HCC data from the Cancer Genome Atlas (TCGA) LIHC cohort. MR analysis and validation identified 21 immune cell subpopulations with significant causal associations to HCC risk. Among these, 12 were identified as risk factors, and 9 as protective factors. Validation in the TCGA cohort revealed that risk-associated immune subpopulations were predominantly enriched for markers of T cell exhaustion and immunosuppressive microenvironments, whereas protective subpopulations likely represented a distinct regulatory B cell subset whose function was associated with the anti-inflammatory factor interleukin-10. This study genetically confirms that specific immune cell functional subpopulations constitute causal risk factors for HCC. These subpopulations exert their effects by shaping distinct tumor immune microenvironments. These findings provide novel mechanisms for understanding the immunopathogenesis of HCC and identify potential targets for developing novel immune intervention strategies.

PMID:41366997 | DOI:10.1097/MD.0000000000045942