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Received β€” 8 January 2026 ⏭ Favourites

Adaptive therapy for perioperative non-small cell lung cancer: strategies guided by dynamic minimal residual disease adjustment

βœ‡Omics In Lung
By: Li-Jin Xie Β· Li-Li Fu Β· Shu-Cen Liu Β· Chang-Sen Bai Β· Bai-Cheng Xu Β· Xiong-Wen He Β· Hai-Feng Lin Β· Yue-Can Zeng Β· Wen-Jun Tang
7 January 2026 at 19:00

Transl Oncol. 2026 Jan 6;64:102660. doi: 10.1016/j.tranon.2025.102660. Online ahead of print.

ABSTRACT

Lung cancer remains the leading cause of cancer incidence and mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for about 85% of cases. The low rate of early diagnosis and the high rate of occult metastases limit the survival benefits of conventional treatments. The current TNM staging system fails to fully reflect tumor heterogeneity or the dynamic molecular evolution of the disease, thus affecting the prediction of recurrence and the prognostic stratification. Some recent advances in minimal residual disease (MRD) detection, such as ultra-sensitive liquid biopsy technologies, have largely overcome the limitations of traditional imaging and offered a transformative approach for continuous, precision-based management of lung cancer. This review systematically summarized the technological evolution of MRD detection and highlighted its clinical significance in guiding adaptive therapy for NSCLC, including treatment escalation, de-escalation, and the emerging concept of precision-guided drug holidays. Moreover, the authors comprehensively discussed the "Four-Dimensional TNMB Staging System," which incorporates continuous molecular monitoring to address the static limitations of conventional staging and enhance the accuracy of prognostic stratification. Although ongoing challenges, such as the lack of standardized interpretation criteria and limited detection sensitivity, the combinations with the third-generation liquid biopsy platforms, multi-omics analyses, and multi-center prospective validation studies are expected to advance the clinical implementation of MRD-guided strategies. The paradigm change will enable the transition of NSCLC management from conventional standardized models to a precision-guided, closed-loop system of "monitoring-intervention-remonitoring," establishing a solid theoretical and practical foundation for comprehensive, molecularly driven management strategies.

PMID:41496417 | DOI:10.1016/j.tranon.2025.102660

Received β€” 21 November 2025 ⏭ Favourites

Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Tiantian Liu Β· Hongwei Yao Β· Feng Lin Β· Tong Wu Β· Zhan Qin Β· Kui Ren
20 November 2025 at 13:00
arXiv:2411.05034v2 Announce Type: replace-cross Abstract: Embeddings have become a cornerstone in the functionality of large language models (LLMs) due to their ability to transform text data into rich, dense numerical representations that capture semantic and syntactic properties. These embedding vector databases serve as the long-term memory of LLMs, enabling efficient handling of a wide range of natural language processing tasks. However, the surge in popularity of embedding vector databases in LLMs has been accompanied by significant concerns about privacy leakage. Embedding vector databases are particularly vulnerable to embedding inversion attacks, where adversaries can exploit the embeddings to reverse-engineer and extract sensitive information from the original text data. Existing defense mechanisms have shown limitations, often struggling to balance security with the performance of downstream tasks. To address these challenges, we introduce Eguard, a novel defense mechanism designed to mitigate embedding inversion attacks. Eguard employs a transformer-based projection network and text mutual information optimization to safeguard embeddings while preserving the utility of LLMs. Our approach significantly reduces privacy risks, protecting over 95% of tokens from inversion while maintaining high performance across downstream tasks consistent with original embeddings.
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