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Received β€” 12 November 2025 ⏭ Favourites

Targeted inhibition of gastric adenocarcinoma by nano-curcumin liposomes: Insights from combined machine learning and experimental analyses into the mechanisms of cuproptosis and metabolic reprogramming

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jun-Feng Cao Β· Kuan Hang Β· Chunlu Tan Β· Zuowei Wu Β· Ziheng Guo Β· Qingjie Xia Β· Dunshui Liao Β· Zengliang Xia Β· Jie Men Β· Kezhou Li
11 November 2025 at 19:00

Int J Pharm. 2025 Nov 9:126368. doi: 10.1016/j.ijpharm.2025.126368. Online ahead of print.

ABSTRACT

PURPOSE: Gastric adenocarcinoma is a highly aggressive malignancy characterized by a complex tumor microenvironment. Nano-curcumin liposomes hold great potential in inhibiting tumor growth and survival, as well as inducing cuproptosis and oxidative stress. Although the anticancer properties of curcumin have been demonstrated, the specific mechanisms by which curcumin inhibites gastric adenocarcinoma through cuproptosis remains unclear. This study investigated how nano-curcumin liposomes mediated the inhibition of gastric adenocarcinoma cell proliferation and survival via cuproptosis.

METHODS: This study utilized the gastric adenocarcinoma cell line AGS to establish 2D and 3D in vitro gastric adenocarcinoma models. Furthermore, we prepared nano-curcumin liposomes to investigate their effects and regulatory mechanisms on AGS gastric adenocarcinoma models. A series of in vitro assays, including flow cytometry, CCK-8, scratch assays and morphological assessments, were performed to evaluate the effects of nano-curcumin liposomes on cell apoptosis, proliferation and migration. Additionally, bioinformatics and machine learning methods were employed to identify key targets that inhibited gastric adenocarcinoma growth and survival associated with nano-curcumin liposomes, which were further validated through RT-qPCR and omics analysis. Computer simulations were also conducted to assess the stability of binding interactions between curcumin and key target proteins.

RESULTS: Cellular experiments demonstrated that nano-curcumin liposomes significantly inhibited proliferation and invasive capacity of gastric adenocarcinoma cells while promoting cellular oxidative stress. Bioinformatics and machine learning analyses identified FDX1, GPX4, SERPINE1 and SLC27A5 as key targets. RT-qPCR results confirmed that nano-curcumin liposomes significantly downregulated the expression of these targets. Molecular dynamics simulations indicated that curcumin could form stable binding interactions with key protein targets.

CONCLUSION: This study revealed that nano-curcumin liposomes inhibited growth and survival of gastric adenocarcinoma cells by interfering with the expression of FDX1, GPX4, SERPINE1 and SLC27A5, which were closely linked to copper-induced oxidative stress. Nano-curcumin liposomes downregulated the expression of FDX1 and GPX4, disrupted mitochondrial energy metabolism, and induced oxidative stress, thereby promoting tumor-associated programmed cell death linked to cuproptosis. Furthermore, by downregulating SERPINE1, nano-curcumin liposomes modulated cell adhesion and migration, inhibiting the invasive and metastatic potential of tumor cells. Finally, downregulation of SLC27A5 altered tumor metabolism and cellular homeostasis, induced oxidative stress, and disrupted intracellular environmental stability, thereby suppressing the growth of gastric adenocarcinoma.

PMID:41218732 | DOI:10.1016/j.ijpharm.2025.126368

Self-Correction Distillation for Structured Data Question Answering

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yushan Zhu Β· Wen Zhang Β· Long Jin Β· Mengshu Sun Β· Ling Zhong Β· Zhiqiang Liu Β· Juan Li Β· Lei Liang Β· Chong Long Β· Chao Deng Β· Junlan Feng
12 November 2025 at 13:00
arXiv:2511.07998v1 Announce Type: cross Abstract: Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are prone to errors in generating structured queries. To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. In SCD, an error prompt mechanism (EPM) is designed to detect errors and provide customized error messages during inference, and a two-stage distillation strategy is designed to transfer large-scale LLMs' query-generation and error-correction capabilities to small-scale LLM. Experiments across 5 benchmarks with 3 structured data types demonstrate that our SCD achieves the best performance and superior generalization on small-scale LLM (8B) compared to other distillation methods, and closely approaches the performance of GPT4 on some datasets. Furthermore, large-scale LLMs equipped with EPM surpass the state-of-the-art results on most datasets.

SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Aladin Djuhera Β· Amin Seffo Β· Vlad C. Andrei Β· Holger Boche Β· Walid Saad
12 November 2025 at 13:00
arXiv:2411.18212v3 Announce Type: replace-cross Abstract: Path planning under wireless performance constraints is a complex challenge in robot navigation. However, naively incorporating such constraints into classical planning algorithms often incurs prohibitive search costs. In this paper, we propose SCoTT, a wireless-aware path planning framework that leverages vision-language models (VLMs) to co-optimize average path gains and trajectory length using wireless heatmap images and ray-tracing data from a digital twin (DT). At the core of our framework is Strategic Chain-of-Thought Tasking (SCoTT), a novel prompting paradigm that decomposes the exhaustive search problem into structured subtasks, each solved via chain-of-thought prompting. To establish strong baselines, we compare classical A* and wireless-aware extensions of it, and derive DP-WA*, an optimal, iterative dynamic programming algorithm that incorporates all path gains and distance metrics from the DT, but at significant computational cost. In extensive experiments, we show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories. Moreover, SCoTT's intermediate outputs can be used to accelerate DP-WA* by reducing its search space, saving up to 62% in execution time. We validate our framework using four VLMs, demonstrating effectiveness across both large and small models, thus making it applicable to a wide range of compact models at low inference cost. We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations. Finally, we discuss data acquisition pipelines, compute requirements, and deployment considerations for VLMs in 6G-enabled DTs, underscoring the potential of natural language interfaces for wireless-aware navigation in real-world applications.
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