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Received β€” 14 March 2026 ⏭

Regulatory mechanisms of ALKBH5/CIITA axis in the synergistic modulation of hepatocellular carcinoma radiotherapy and immunotherapy

βœ‡Omics in Hepatocellular
By: Fei Wang Β· Hui Hou Β· Hong Yang Β· Ting Dou Β· Zhenxia Wang Β· Hongdan Fu Β· Hong Li Β· Wei Zhang Β· Haiping Zhao Β· Hao Yang
11 March 2026 at 18:00

Genes Immun. 2026 Mar 10. doi: 10.1038/s41435-026-00382-6. Online ahead of print.

ABSTRACT

The prognosis for hepatocellular carcinoma remains grim. Combining radiotherapy with immune checkpoint blockade (ICB) has shown potential to enhance therapeutic outcomes, yet there is a pressing need for further advancements. Our previous research demonstrated that this combined approach suppresses ALKBH5 gene expression and increases m6A modification levels in hepatocellular carcinoma tissues. High-throughput sequencing and detailed molecular analysis revealed that inhibiting ALKBH5 amplifies CIITA m6A modifications post-therapy. This modulation triggers MHC II molecule expression in tumors, facilitating the presentation of tumor-associated antigens to CD4 + T lymphocytes and the recruitment of CD8 + T cells for an anti-tumor immune response. Building on these findings, we engineered a CIITA vector with a specific site mutation to confirm that the regulation of CIITA by the combined radiotherapy and immunotherapy is mediated through m6A methylation. Consequently, we established a comprehensive network involving ALKBH5, CIITA, MHC II, and CD4+ and CD8 + T cells. To elucidate the role and underlying molecular mechanisms of this combined therapy in reshaping the tumor immune microenvironment for hepatocellular carcinoma, we employed multi-omics approaches across in vitro, animal model, and clinical multi-dimensional studies, offering novel insights for enhancing treatment efficacy.

PMID:41807814 | DOI:10.1038/s41435-026-00382-6

Received β€” 11 March 2026 ⏭

Long-Short Term Agents for Pure-Vision Bronchoscopy Robotic Autonomy

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Junyang Wu Β· Mingyi Luo Β· Fangfang Xie Β· Minghui Zhang Β· Hanxiao Zhang Β· Chunxi Zhang Β· Junhao Wang Β· Jiayuan Sun Β· Yun Gu Β· Guang-Zhong Yang
10 March 2026 at 12:00
arXiv:2603.07909v1 Announce Type: cross Abstract: Accurate intraoperative navigation is essential for robot-assisted endoluminal intervention, but remains difficult because of limited endoscopic field of view and dynamic artifacts. Existing navigation platforms often rely on external localization technologies, such as electromagnetic tracking or shape sensing, which increase hardware complexity and remain vulnerable to intraoperative anatomical mismatch. We present a vision-only autonomy framework that performs long-horizon bronchoscopic navigation using preoperative CT-derived virtual targets and live endoscopic video, without external tracking during navigation. The framework uses hierarchical long-short agents: a short-term reactive agent for continuous low-latency motion control, and a long-term strategic agent for decision support at anatomically ambiguous points. When their recommendations conflict, a world-model critic predicts future visual states for candidate actions and selects the action whose predicted state best matches the target view. We evaluated the system in a high-fidelity airway phantom, three ex vivo porcine lungs, and a live porcine model. The system reached all planned segmental targets in the phantom, maintained 80\% success to the eighth generation ex vivo, and achieved in vivo navigation performance comparable to the expert bronchoscopist. These results support the preclinical feasibility of sensor-free autonomous bronchoscopic navigation.

EndoSERV: A Vision-based Endoluminal Robot Navigation System

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Junyang Wu Β· Fangfang Xie Β· Minghui Zhang Β· Hanxiao Zhang Β· Jiayuan Sun Β· Yun Gu Β· Guang-Zhong Yang
10 March 2026 at 12:00
arXiv:2603.08324v1 Announce Type: cross Abstract: Robot-assisted endoluminal procedures are increasingly used for early cancer intervention. However, the intricate, narrow and tortuous pathways within the luminal anatomy pose substantial difficulties for robot navigation. Vision-based navigation offers a promising solution, but existing localization approaches are error-prone due to tissue deformation, in vivo artifacts and a lack of distinctive landmarks for consistent localization. This paper presents a novel EndoSERV localization method to address these challenges. It includes two main parts, \textit{i.e.}, \textbf{SE}gment-to-structure and \textbf{R}eal-to-\textbf{V}irtual mapping, and hence the name. For long-range and complex luminal structures, we divide them into smaller sub-segments and estimate the odometry independently. To cater for label insufficiency, an efficient transfer technique maps real image features to the virtual domain to use virtual pose ground truth. The training phases of EndoSERV include an offline pretraining to extract texture-agnostic features, and an online phase that adapts to real-world conditions. Extensive experiments based on both public and clinical datasets have been performed to demonstrate the effectiveness of the method even without any real pose labels.
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