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Received β€” 11 December 2025 ⏭ Favourites

Lung Cancer Diagnosis and Prognostic Monitoring Through Cell-Free RNA via Liquid Biopsy

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yuanming Pan Β· Chongbo Jiang Β· Mengchan Ye Β· Dongmei Li Β· Jinghui Wang
10 December 2025 at 19:00

Ther Clin Risk Manag. 2025 Dec 2;21:1615-1636. doi: 10.2147/TCRM.S542338. eCollection 2025.

ABSTRACT

Lung cancer remains a leading cause of cancer-related mortality worldwide, largely due to challenges in its early detection and effective management. Despite advances in treatment modalities, the complex nature of lung cancer, characterized by its molecular heterogeneity and resistance mechanisms, underscores the need for innovative approaches. Cell-free RNA (cfRNA) has emerged as a promising biomarker with significant clinical applications in lung cancer diagnosis, monitoring, and precision medicine. We explore key themes including the utility of cfRNA in early detection, differentiation between benign and malignant lung nodules, molecular subtyping, and real-time therapeutic monitoring. Advances in liquid biopsy technologies, particularly non-invasive cfRNA analysis, provide dynamic means of tracking tumor evolution. cfRNA biomarkers such as miRNA, long non-coding RNAs, and circular RNAs offer unique insights into tumor biology, paving the way for personalized treatment strategies. Further, we discuss the application of cutting-edge technologies such as AI-driven analytics, next-generation sequencing, and multi-omics integration, which are enhancing the clinical utility of cfRNA in identifying treatment resistance and improving outcomes in immunotherapy, targeted therapy, and chemotherapy. The review addresses significant challenges facing cfRNA applications, including pre-analytical variability, technical limitations in detection methods, economic constraints, and the lack of standardization in clinical protocols. Through multidisciplinary collaborations and standardized methodologies, significant progress can be made toward integrating cfRNA into routine clinical practice. Emphasis is placed on future research directions, which include validating cfRNA biomarkers across diverse populations, streamlining workflows, and addressing scalability issues for real-world applications. This comprehensive exploration positions cfRNA at the forefront of innovations in lung cancer management, offering a pathway for improved diagnostic accuracy and individualized care.

PMID:41367889 | PMC:PMC12682701 | DOI:10.2147/TCRM.S542338

Received β€” 18 November 2025 ⏭ Favourites

EduAgentQG: A Multi-Agent Workflow Framework for Personalized Question Generation

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Rui Jia Β· Min Zhang Β· Fengrui Liu Β· Bo Jiang Β· Kun Kuang Β· Zhongxiang Dai
18 November 2025 at 13:00
arXiv:2511.11635v1 Announce Type: cross Abstract: High-quality personalized question banks are crucial for supporting adaptive learning and individualized assessment. Manually designing questions is time-consuming and often fails to meet diverse learning needs, making automated question generation a crucial approach to reduce teachers' workload and improve the scalability of educational resources. However, most existing question generation methods rely on single-agent or rule-based pipelines, which still produce questions with unstable quality, limited diversity, and insufficient alignment with educational goals. To address these challenges, we propose EduAgentQG, a multi-agent collaborative framework for generating high-quality and diverse personalized questions. The framework consists of five specialized agents and operates through an iterative feedback loop: the Planner generates structured design plans and multiple question directions to enhance diversity; the Writer produces candidate questions based on the plan and optimizes their quality and diversity using feedback from the Solver and Educator; the Solver and Educator perform binary scoring across multiple evaluation dimensions and feed the evaluation results back to the Writer; the Checker conducts final verification, including answer correctness and clarity, ensuring alignment with educational goals. Through this multi-agent collaboration and iterative feedback loop, EduAgentQG generates questions that are both high-quality and diverse, while maintaining consistency with educational objectives. Experiments on two mathematics question datasets demonstrate that EduAgentQG outperforms existing single-agent and multi-agent methods in terms of question diversity, goal consistency, and overall quality.
Received β€” 24 December 2024 ⏭ Favourites

Cancer-associated fibroblasts promote the stemness and progression of renal cell carcinoma via exosomal miR-181d-5p

βœ‡Cell Death Discovery nature.com science feeds
By: Meng Ding Β· Xiaozhi Zhao Β· Xiaoqing Chen Β· Wenli Diao Β· Yansheng Kan Β· Wenmin Cao Β· Wei Chen Β· Bo Jiang Β· Haixiang Qin Β· Jie Gao Β· Junlong Zhuang Β· Qing Zhang Β· Hongqian Guo
1 November 2022 at 08:00

Cell Death Discovery, Published online: 01 November 2022; doi:10.1038/s41420-022-01219-7

Cancer-associated fibroblasts promote the stemness and progression of renal cell carcinoma via exosomal miR-181d-5p
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