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Small extracellular vesicle-mediated metabolic reprogramming: from tumors to pre-metastatic niche formation

CELL COMMUNICATION AND SIGNALING 2023年第1期21卷 1-15页

作者： jiang, chuwen jiang, Zhengting Sha, Gengyu Wang, Daorong Tang, Dong Yangzhou Univ Clin Med Coll Yangzhou 225001 Jiangsu Peoples R China Yangzhou Univ Northern Jiangsu Peoples Hosp Inst Gen Surg Clin Med CollDept Gen Surg Yangzhou 225001 Peoples R China

Metastasis, the spread of a tumor or cancer from the primary site of the body to a secondary site, is a multi-step process in cancer progression, accounting for various obstacles in cancer treatment and most cancer-related deaths. Metabolic reprogramming refers to adaptive metabolic changes that occur in cancer cells in the tumor microenvironment (TME) to enhance their survival ability and metastatic potential. Stromal cell metabolism also changes to stimulate tumor proliferation and metastasis. Metabolic adaptations of tumor and non-tumor cells exist not only in the TME but also in the pre-metastatic niche (PMN), a remote TME conducive for tumor metastasis. As a novel mediator in cell-to-cell communication, small extracellular vesicles (sEVs), which have a diameter of 30-150 nm, reprogram metabolism in stromal and cancer cells within the TME by transferring bioactive substances including proteins, mRNAs and miRNAs (microRNAs). sEVs can be delivered from the primary TME to PMN, affecting PMN formation in stroma rewriting, angiogenesis, immunological suppression and matrix cell metabolism by mediating metabolic reprogramming. Herein, we review the functions of sEVs in cancer cells and the TME, how sEVs facilitate PMN establishment to trigger metastasis via metabolic reprogramming, and the prospective applications of sEVs in tumor diagnosis and treatment.

关键词： sEVs Metabolic reprogramming Tumor microenvironment PMN Metastasis

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The engineered exosomes targeting ferroptosis: A novel approach to reverse immune checkpoint inhibitors resistance

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INTERNATIONAL JOURNAL OF CANCER 2024年第1期155卷 7-18页

作者： Chen, Anqi Zhang, Wenjie jiang, chuwen jiang, Zhengting Tang, Dong Yangzhou Univ Med Coll Yangzhou Jiangsu Peoples R China Chongqing Univ Sch Med Chongqing Peoples R China Yangzhou Univ Northern Jiangsu Peoples Hosp Clin Med Coll Dept Gen SurgInst Gen Surg Yangzhou 225001 Peoples R China Yangzhou Univ Northern Jiangsu Peoples Hosp Clin Med Coll Dept Gen SurgInst Gen Surg Yangzhou 225001 Peoples R China

Immune checkpoint inhibitors (ICIs) have been extensively used in immunological therapy primarily due to their ability to prolong patient survival. Although ICIs have achieved success in cancer treatment, the resistance of ICIs should not be overlooked. Ferroptosis is a newly found cell death mode characterized by the accumulation of reactive oxygen species (ROS), glutathione (GSH) depletion, and glutathione peroxidase 4 (GPX4) inactivation, which has been demonstrated to be beneficial to immunotherapy and combining ferroptosis and ICIs to exploit new immunotherapies may reverse ICIs resistance. Exosomes act as mediators in cell-to-cell communication that may regulate ferroptosis to influence immunotherapy through the secretion of biological molecules. Thus, utilizing exosomes to target ferroptosis has opened up exciting possibilities for reversing ICIs resistance. In this review, we summarize the mechanisms of ferroptosis improving ICIs therapy and how exosomes regulate ferroptosis through adjusting iron metabolism, blocking the ROS accumulation, controlling ferroptosis defense systems, and influencing classic signaling pathways and how engineered exosomes target ferroptosis and improve ICIs efficiency. image

关键词： engineered exosomes exosomes ferroptosis ICIs resistance immunotherapy

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Ensemble methods based on characterization of dynamism for dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2022年 129卷

作者： jiang, chuwen Ge, Fangzhen Chen, Debao Liu, Huaiyu Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei Anhui Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol 100Dongshan Rd Huaibei 235000 Peoples R China

Dynamic multi-objective optimization problems involve multiple conflicting and time-dependent objectives that change continuously depending on the environment. Therefore, effectively tracking the movement of the Pareto-optimal front and the Pareto-optimal set (PS) using a single strategy is difficult. In this study, we propose an ensemble method called EMCD, which is based on the character-ization of dynamism. EMCD selects different strategies to generate new populations depending on the characteristics of environmental changes. In our proposed algorithm, historical information is used to locate new populations when the current and historical environmental changes are similar. Otherwise, EMCD establishes whether the PS has changed. If the PS changes, individuals from the population of the new environment are generated by using a set of knee points based on the two previous consecutive populations. If not, new populations are generated by using the perturbation-based approach. In addition, we use an adaptive diversity introduction strategy to ensure the solvability of our proposed algorithm in dynamic environments. To verify the effectiveness of EMCD, we compare its performance to that of three state-of-the-art algorithms on fourteen benchmark problems. The experimental results show that EMCD outperforms the comparison algorithms on most of the benchmark problems.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Dynamic multi-objective optimization Evolutionary algorithm Similar environment Memory Prediction Adaptive diversity introduction

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An Enhanced Alternating Direction Method of Multipliers-Based Interior Point Method for Linear and Conic Optimization

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INFORMS JOURNAL ON COMPUTING 2025年第2期37卷 197-502, C2页

作者： Deng, Qi Feng, Qing Gao, Wenzhi Ge, Dongdong jiang, Bo jiang, Yuntian Liu, Jingsong Liu, Tianhao Xue, Chenyu Ye, Yinyu Zhang, chuwen Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200030 Peoples R China Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA Stanford Univ Inst Computat & Math Engn Palo Alto CA 94305 USA Shanghai Univ Finance & Econ Res Inst Interdisciplinary Sci Sch Informat Management & Engn Shanghai 200433 Peoples R China Shanghai Univ Finance & Econ Key Lab Interdisciplinary Res Computat & Econ Shanghai 200433 Peoples R China Shanghai Univ Finance & Econ Dishui Lake Adv Finance Inst Shanghai 200120 Peoples R China

The alternating-direction-method-of-multipliers-based (ADMM-based) interior point method, or ABIP method, is a hybrid algorithm that effectively combines interior point method (IPM) and first-order methods to achieve a performance boost in large-scale linear optimization. Different from traditional IPM that relies on computationally intensive Newton steps, the ABIP method applies ADMM to approximately solve the barrier penalized problem. However, similar to other first-order methods, this technique remains sensitive to condition number and inverse precision. In this paper, we provide an enhanced ABIP method with multiple improvements. First, we develop an ABIP method to solve the general linear conic optimization and establish the associated iteration complexity. Second, inspired by some existing methods, we develop different implementation strategies for the ABIP method, which substantially improve its performance in linear optimization. Finally, we conduct extensive numerical experiments in both synthetic and real-world data sets to demonstrate the empirical advantage of our developments. In particular, the enhanced ABIP method achieves a 5.8x reduction in the geometric mean of run time on 105 selected linear optimization instances from Netlib, and it exhibits advantages in certain structured problems, such as support vector machine and PageRank. However, the enhanced ABIP method still falls behind commercial solvers in many benchmarks, especially when high accuracy is desired. We posit that it can serve as a complementary tool alongside wellestablished solvers.

关键词： linear optimization conic optimization ADMM interior point method implementation improvement iteration complexity

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The multifunction of HSP70 in cancer: Guardian or traitor to the survival of tumor cells and the next potential therapeutic target

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INTERNATIONAL IMMUNOPHARMACOLOGY 2023年 122卷 110492页

作者： Sha, Gengyu jiang, Zhengting Zhang, Wenjie jiang, chuwen Wang, Daorong Tang, Dong Yangzhou Univ Clin Med Coll Yangzhou 225000 Jiangsu Peoples R China Yangzhou Univ Northern Jiangsu Peoples Hosp Inst Gen Surg Clin Med CollDept Gen Surg Yangzhou 225000 Peoples R China

Heat shock protein 70 (HSP70) is a highly conserved protein composed of nucleotide-binding domains (NBD) and C-terminal substrate binding domain (SBD) that can function as a "molecular chaperone". HSP70 was discovered to directly or indirectly play a regulatory role in both internal and external apoptosis pathways. Studies have shown that HSP70 can not only promote tumor progression, enhance tumor cell resistance and inhibit anticancer effects but also induce an anticancer response by activating immune cells. In addition, chemotherapy, radio-therapy and immunotherapy for cancer may be affected by HSP70, which has shown promising potential as an anticancer drug. In this review, we summarized the molecular structure and mechanism of HSP70 and discussed the dual effects of HSP70 on tumor cells and the possibility and potential methods of using HSP70 as a target to treat cancer.

关键词： HSP70 Cancer Versatility Immunological therapy

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CmBBX28-CmMYB9a Module Regulates Petal Anthocyanin Accumulation in Response to Light in Chrysanthemum

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PLANT CELL AND ENVIRONMENT 2025年第5期48卷 3750-3765页

作者： Zhou, Li-Jie Peng, Jialin Chen, chuwen Wang, Yiguang Wang, Yuxi Li, Yanan Song, Aiping jiang, Jiafu Chen, Sumei Chen, Fadi Minist Agr & Rural Affairs State Key Lab Crop Genet & Germplasm Enhancement & Key Lab Landscaping Nanjing Jiangsu Peoples R China Zhongshan Biol Breeding Lab Key Lab State Forestry & Grassland Adm Biol Orname Nanjing Jiangsu Peoples R China Nanjing Agr Univ Coll Hort Nanjing Jiangsu Peoples R China

Light is one of the most important environmental factors that affect plant growth and development. It also stimulates anthocyanin biosynthesis in plants. However, the precise molecular mechanisms through which light regulates anthocyanin biosynthesis, particularly in non-model plant species, remain poorly understood. In this study, we discovered a CmBBX28-CmMYB9a molecular module that is responsive to light and regulates anthocyanin biosynthesis in chrysanthemums. Specifically, CmBBX28 interacts with CmMYB9a, interfering with its binding to the promoters of target genes and reducing the protein abundance of CmMYB9a. This interaction downregulates the transcription of CmMYB9a's downstream anthocyanin-associated genes, CmCHS, CmDFR, and CmUFGT. The expression of CmBBX28 was induced in the dark, and the accumulated CmBBX28 proteins interfered with the activation of CmMYB9a during anthocyanin biosynthesis. Concurrently, darkness also inhibited the expression of CmMYB9a to some extent. In contrast, light significantly induced the expression of CmMYB9a and suppressed the expression of CmBBX28, resulting in increased anthocyanin accumulation in chrysanthemum petals. Our findings reveal the mechanism by which light regulates anthocyanin biosynthesis in chrysanthemum flower petals.

关键词： anthocyanin biosynthesis chrysanthemum CmBBX28 CmMYB9a light

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Homogeneous second-order descent framework: a fast alternative to Newton-type methods

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Mathematical Programming 2025年 1-62页

作者： He, Chang jiang, Yuntian Zhang, chuwen Ge, Dongdong jiang, Bo Ye, Yinyu School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai 200433 China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai 200030 China Department of Management Science and Engineering Stanford University Stanford 94305 CA United States

This paper proposes a homogeneous second-order descent framework (HSODF) for nonconvex and convex optimization based on the generalized homogeneous model (GHM). In comparison to the Newton steps, the GHM can be solved by extremal symmetric eigenvalue procedures and thus grant an advantage in ill-conditioned problems. Moreover, GHM extends the ordinary homogeneous model (Zhang et al. A homogenous second-order descent method for nonconvex optimization, 2022. arXiv:2211.08212 [math]) to allow adaptiveness in the construction of the aggregated matrix. Consequently, HSODF is able to recover some well-known second-order methods, such as trust-region methods and gradient regularized methods, while maintaining comparable iteration complexity bounds. We also study two specific realizations of HSODF. One is adaptive HSODM, which has a parameter-free O(ϵ-3/2) global complexity bound for nonconvex second-order Lipschitz continuous objective functions. The other is homotopy HSODM, which is proven to have a global linear rate of convergence without strong convexity. The efficiency of our approach to high-dimensional and ill-conditioned problems is justified by some preliminary numerical results. © Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2025.

关键词： Large-scale optimization Second-order method Worst case complexity

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Advanced Digital Simulation for Financial Market Dynamics: A Case of Commodity Futures

arXiv

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arXiv 2025年

作者： Wang, Cheng Wang, chuwen Zeng, Shirong jiang, Changjun School of Computer Science and Technology Tongji University China

After decades of evolution, the financial system has increasingly deviated from an idealized framework based on precise theorems. It necessitates accurate projections of complex market dynamics and human behavioral patterns. With the development of data science and machine intelligence, researchers are trying to digitalize and automate market prediction. However, existing methodologies struggle to represent the diversity of individuals and are regardless of the domino effects of interactions on market dynamics, leading to the poor performance facing abnormal market conditions where non-quantitative information dominates the market. To alleviate these disadvantages requires the introduction of knowledge about how non-quantitative information, like news and policy, affects market dynamics. This study investigates overcoming these challenges through rehearsing potential market trends based on the financial large language model agents whose behaviors are aligned with their cognition and analyses in markets. We propose a hierarchical knowledge architecture for financial large language model agents, integrating fine-tuned language models and specialized generators optimized for trading scenarios. For financial market, we develop an advanced interactive behavioral simulation system that enables users to configure agents and automate market simulations. In this work, we take commodity futures as an example to research the effectiveness of our methodologies. Our real-world case simulation succeeds in rehearsing abnormal market dynamics under geopolitical events and reaches an average accuracy of 3.4% across various points in time after the event on predicting futures price. Under normal market conditions, with corresponding news, our simulator also exhibits lower mean square error than series deep learning models and large language models in predicting three-day futures price of specific commodities. All experimental results demonstrate our method effectively leverages diver

关键词： Financial markets

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胰岛自身抗体和生化指标在糖尿病分型诊断中的应用价值

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重庆医学 2016年第21期45卷 2909-2913页

作者：周晖李正康徐晓明江楚文严永智裴剑浩广东省医学科学院/广东省人民医院检验科广州510080 广东省广州市妇女儿童医疗中心 510623 广东省医学科学院/广东省人民医院省临床检验中心广州510120 广东省医学科学院/广东省人民医院内分泌科广州510080

目的探讨胰岛自身抗体与生化指标在1型糖尿病（T1DM）和2型糖尿病（T2DM）临床分型诊断中的应用价值。方法回顾性分析99例T1DM患者（T1DM组）与577例T2DM患者（T2DM）的临床资料及实验室检测结果,比较两组胰岛自身抗体及各项生化指标水... 详细信息

目的探讨胰岛自身抗体与生化指标在1型糖尿病（T1DM）和2型糖尿病（T2DM）临床分型诊断中的应用价值。方法回顾性分析99例T1DM患者（T1DM组）与577例T2DM患者（T2DM）的临床资料及实验室检测结果,比较两组胰岛自身抗体及各项生化指标水平,并分析其特征。结果 T1DM组患者血清谷氨酸脱羧酶抗体（GADA）、胰岛细胞瘤相关蛋白-2自身抗体（IA-2A）、抗胰岛细胞抗体（ICA）及锌转运体8自身抗体（ZnT8A）单项及联合检测的阳性率均高于T2DM组,差异均有统计学意义（P〈0.05）。T1DM组患者起病年龄、空腹C肽（CP）、餐后2hCP、空腹胰岛素（INS）、餐后2hINS、三酰甘油（TG）和体质量指数（BMI）均低于T2DM组,空腹血糖、餐后2h血糖、糖化血红蛋白（HbA1c）及高密度脂蛋白胆固醇（HDL-C）水平均高于T2DM组,差异均有统计学意义（P〈0.05）;而两组总胆固醇、低密度脂蛋白胆固醇水平比较,差异均无统计学意义（P〉0.05）。空腹CP及餐后2hCP水平在T1DM组均随着病程延长呈下降趋势,且不同病程间比较,差异均有统计学意义（P〈0.05）;而在T2DM组无明显的下降趋势。病程小于2年的患者空腹CP、餐后2hCP鉴别诊断T1DM与T2DM的受试者工作曲线下面积及其95%置信区间分别为0.902（0.850-0.954）、0.905（0.852-0.958）。当空腹CP诊断界点为0.283nmol/L时,其灵敏度和特异度分别为82.6%、89.2%;餐后2hCP诊断界点为0.421nmol/L时,其灵敏度和特异度分别为84.8%、89.2%。结论T1DM、T2DM患者的起病年龄、BMI值及血清GADA、IA-2A、ICA、ZnT8A、INS、CP、葡萄糖、HbA1c、TG、HDL-C水平等指标存在差异,可辅助临床对其进行分型诊断。

关键词： C肽自身抗体血液化学分析糖尿病,1型糖尿病,2型

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基于迁移学习的拐点预测策略求解动态多目标优化问题

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青岛科技大学学报（自然科学版） 2021年第4期42卷 102-112页

作者：江储文葛方振刘怀愚高向军沈龙凤淮北师范大学计算机科学与技术学院安徽淮北235000

动态多目标优化问题具有多个相互冲突的目标,而且这些目标也受环境的影响不断变化,为了快速准确跟踪不断变化的Pareto前沿和Pareto解集,提出一种基于迁移学习的拐点预测策略(a knee points prediction strategy based on transfer learn... 详细信息

动态多目标优化问题具有多个相互冲突的目标,而且这些目标也受环境的影响不断变化,为了快速准确跟踪不断变化的Pareto前沿和Pareto解集,提出一种基于迁移学习的拐点预测策略(a knee points prediction strategy based on transfer learning, TKPS)。TKPS根据记忆过去时刻种群中优秀个体,使用迁移学习算法得到映射矩阵W,然后通过映射矩阵W,把当前时刻的拐点集映射到高维希尔伯特空间,从中找到下一时刻的拐点集,引导种群收敛;同时,在拐点的邻域内选出若干个伴随个体,增加种群多样性,避免种群陷入局部最优。TKPS采用8个测试函数,并与其它3个算法结果对比分析,实验结果表明TKPS算法具有更快的响应环境变化的能力。

关键词：动态多目标优化迁移学习拐点预测

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