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Self-sufficient nanoparticles with dual-enzyme activity trigger radical storms and activate cascade-amplified antitumor immunologic responses

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Acta Pharmaceutica Sinica B 2024年第2期14卷 821-835页

作者： Liping Bai Jin Yang Siting Yu Zhongzheng Xiang yuanyuan zeng Meiling Shen Xiaorong Kou Qinjie Wu Changyang Gong Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu 610041China

Radiotherapy(RT) can potentially induce systemic immune responses by initiating immunogenic cell death(ICD) of tumor cells.However,RT-induced antitumor immunologic responses are sporadic and insufficient against cancer metastases.Herein,we construct multifunctional self-sufficient nanoparticles(MARS) with dual-enzyme activity(GOx and peroxidase-like) to trigger radical storms and activate the cascade-amplified systemic immune responses to suppress both local tumors and metastatic relapse.In addition to limiting the Warburg effect to actualize starvation therapy,MARS catalyzes glucose to produce hydrogen peroxide(H_(2)O_(2)),which is then used in the Cu^(+)-mediated Fenton-like reaction and RT sensitization.RT and chemodynamic therapy produce reactive oxygen species in the form of radical storms,which have a robust ICD impact on mobilizing the immune system.Thus,when MARS is combined with RT,potent systemic antitumor immunity can be generated by activating antigen-presenting cells,promoting dendritic cells maturation,increasing the infiltration of cytotoxic T lymphocytes,and reprogramming the immuno suppre ssive tumor microenvironment.Furthermore,the synergistic therapy of RT and MARS effectively suppresses local tumor growth,increases mouse longevity,and results in a 90% reduction in lung metastasis and postoperative recurrence.Overall,we provide a viable approach to treating cancer by inducing radical storms and activating cascade-amplified systemic immunity.

关键词： Dual-enzyme activity Radiotherapy Chemodynamic therapy Fenton-likereaction Radical storms Immunogeniccell death Tumormicroenvironment Systemicimmunity

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Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy:Combined machine learning and experimental validation in mice

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BIOCELL 2023年第6期47卷 1253-1263页

作者： WEI LIANG ZONGWEI ZHANG KEJU YANG HONGTU HU QIANG LUO ANKANG YANG LI CHANG yuanyuan zeng Division of Nephrology Renmin Hospital of Wuhan UniversityWuhanChina Nephrology and Urology Research Institute of Wuhan University WuhanChina The First College of Clinical Medical Science Three Gorges UniversityYichangChina School of Life Science and Engineering Southwest Jiaotong UniversityChengduChina

Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

关键词： Diabetic nephropathy Renal tubule Machine learning Diagnostic model Genetic algorithm

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脓毒症患者发生正常甲状腺性病态综合征的相关因素

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北京大学学报（医学版） 2024年第3期56卷 526-532页

作者：曾媛媛谢云陈道南王瑞兰南京医科大学附属上海一院临床医学院急诊危重病科上海201620

目的:评估脓毒症患者正常甲状腺性病态综合征(euthyroid sick syndrome,ESS)的患病率及其影响因素。方法:选择2017年1月—2023年1月上海市第一人民医院急诊危重病科诊断为脓毒症的365例患者的病例资料进行回顾性分析,收集患者的相关临... 详细信息

目的:评估脓毒症患者正常甲状腺性病态综合征(euthyroid sick syndrome,ESS)的患病率及其影响因素。方法:选择2017年1月—2023年1月上海市第一人民医院急诊危重病科诊断为脓毒症的365例患者的病例资料进行回顾性分析,收集患者的相关临床数据。根据是否合并ESS,将患者分为ESS组(103例)和非ESS组(262例)。评估脓毒症患者ESS的患病率,对其影响因素进行多因素Logistic回归分析,比较两组患者30 d的生存率,探索游离三碘甲腺原氨酸(free triiodothyronine,FT3)预测脓毒症患者死亡的最佳截断值。结果:脓毒症患者中合并ESS者有103例,占总病例数的28.2%。ESS组脓毒症严重程度重于非ESS组,差异有统计学意义(P<0.05);ESS组急性生理学和慢性健康评估Ⅱ(acute physiology and chronic health evaluationⅡ,APACHEⅡ)、序贯器官衰竭(sequential organ failure assessment,SOFA)评分明显高于非ESS组(P<0.05);ESS组C反应蛋白(C-reactive protein,CRP)、降钙素原(procalcitonin,PCT)、血清淀粉样蛋白A(serum amyloid A,SAA)、白介素-6(interleukin-6,IL-6)均高于非ESS组;ESS组总胆固醇(total cholesterol,TC)、高密度脂蛋白胆固醇(high-density liptein cholesterol,HDL-C)低于非ESS组,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,PCT、IL-6、CRP、SAA、部分激活的凝血活酶时间(activated partial thromboplatin time,APTT)是脓毒症患者ESS发生的独立危险因素(OR值分别为1.105、1.006、1.005、1.009、1.033,95%CI分别为1.044~1.170、1.001~1.012、1.001~1.009、1.005~1.014、1.004~1.062,P<0.05)。ESS组30 d生存率显著低于非ESS组,Long-rank卡方检验值16.611,差异有统计学意义(P<0.05)。脓毒症患者FT3预测死亡的受试者工作特征曲线下面积(area under the curve,AUC)为0.924(95%CI 0.894~0.954)。血清FT3截断点为3.705 pmol/L,特异性为0.868,敏感性为0.950。结论:脓毒症患者的ESS发生率为28.2%,且预后不良;PCT、IL-6、CRP、SAA、APTT是脓毒症患者ESS发生的独立危险因素,HDL-C是保护性因素;FT3是脓毒症患者预测死亡的潜在新型生物标志物。

关键词：正常甲状腺性病态综合征脓毒症患病率影响因素预后

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结合铁死亡建立遗传算法优化的反向传播神经网络脓毒症预后模型

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中国医科大学学报 2024年第4期53卷 295-301页

作者：曾媛媛常莉池晴佳封顺田菲菲西南交通大学生命科学与工程学院生物工程系成都610036 武汉理工大学理学院工程结构与力学系武汉430070

目的将铁死亡与机器学习相结合,建立一种基于遗传算法的反向传播网络(GA-BPNN)模型,以预测脓毒症患者的28 d存活情况。方法通过高通量基因表达数据库(GEO)下载脓毒症相关数据。从分子签名数据库(MSigDB)下载与铁死亡相关的基因65个。使... 详细信息

目的将铁死亡与机器学习相结合,建立一种基于遗传算法的反向传播网络(GA-BPNN)模型,以预测脓毒症患者的28 d存活情况。方法通过高通量基因表达数据库(GEO)下载脓毒症相关数据。从分子签名数据库(MSigDB)下载与铁死亡相关的基因65个。使用基因集富集分析(GSEA)、基因集变异分析(GSVA)、加权基因共表达网络分析(WGCNA)和蛋白质-蛋白质相互作用(PPI)网络分析筛选脓毒症患者的铁死亡相关基因。同时利用短时间序列挖掘分析(STEM)筛选与脓毒症发展进程相关的基因,并与脓毒症患者铁死亡相关基因取交集,获得脓毒症预后的关键基因。在传统的反向传播网络(BPNN)的基础上,采用遗传算法(GA)优化权值和阈值,建立脓毒症预后预测GA-BPNN模型。结果脓毒症存活组和未存活组之间铁死亡相关基因集活性差异显著。共筛选出97个脓毒症铁死亡相关基因。同时确定了191个在脓毒症发展进程中显著上调或下调的基因。取交集基因建立脓毒症四基因预测预后GA-BPNN模型。在训练集中经过迭代后训练的均方误差在0.05以下,曲线下面积(AUC)为0.98。为验证GA-BPNN模型的泛化能力和分类效果,在外部数据验证集中,将GA-BPNN模型与支持向量机(SVM)、BPNN和随机森林(RF)进行比较。结果表明,在验证集中GA-BPNN模型的AUC值均为0.92,高于SVM、RF和BPNN。结论基于GA-BPNN提出了四基因模型预测脓毒症患者的预后效果是可靠且稳定的,为脓毒症中的铁死亡提供新的见解。

关键词：脓毒症铁死亡生物标志物预后模型

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A Swarm Intelligence Networking Framework for Small Satellite Systems

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Communications and Network 2013年第3期5卷 171-175页

作者： Zijing Chen yuanyuan zeng The Academy of Satellite Application Beijing China The Academy of Satellite Application Beijing China School of Electronic Information Wuhan University Wuhan China

Recent development of technologies and methodologies on distributed spacecraft systems enable the small satellite network systems by supporting integrated navigation, communications and control tasks. The distributed sensing data can be communicated and processed autonomously among the network systems. Due to the size, density and dynamic factors of small satellite networks, the traditional network communication framework is not well suited for distributed small satellites. The paper proposes a novel swarm intelligence based networking framework by using Ant colony optimization. The proposed network framework enables self-adaptive routing, communications and network reconstructions among small satellites. The simulation results show our framework is suitable for dynamic factors in distributed small satellite systems. The proposed schemes are adaptive and scalable to network topology and achieve good performance in different network scenarios.

关键词： Small Satellite Systems Ant Colony Optimization Swarm Intelligence Network Reconstruction

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黄柏炮制历史沿革及其药理作用研究

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辽宁中医药大学学报 2024年第3期26卷 194-199页

作者：李小宁田辉曾媛媛贺霞陕西省药品和疫苗检查中心陕西西安710065 陕西省药品和疫苗检查中心咸阳分中心陕西咸阳712300

黄柏作为传统中药之一,具有清热燥湿、泻火除蒸、解毒疗疮之功效,其炮制历史悠久,炮制方法众多。通过查阅历代本草、医学古籍和相关文献资料,对黄柏的历史炮制方法进行整理和归纳,主要包括净制法、切制法和炮制法,其中炮制法分为清炒法... 详细信息

黄柏作为传统中药之一,具有清热燥湿、泻火除蒸、解毒疗疮之功效,其炮制历史悠久,炮制方法众多。通过查阅历代本草、医学古籍和相关文献资料,对黄柏的历史炮制方法进行整理和归纳,主要包括净制法、切制法和炮制法,其中炮制法分为清炒法和加辅料制法,清炒法有炒、炙、焙、炮、煨、蒸;加辅料的炮制方法分为酒制、盐制、醋制、蜜制、姜制、童便制等多种炮制方法,而现代应用多以酒制黄柏、盐制黄柏为主。同时,对黄柏近年来的药理作用进行总结,主要集中在治抗动脉粥样硬化、抗溃疡、抗痛风、抗肿瘤、降糖、神经保护、止泻、抑菌等方面,以期更深入理解黄柏的药理作用及其内在机制。

关键词：黄柏炮制历史沿革现代炮制药理作用

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2023年生成式人工智能技术主要发展动向分析

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无人系统技术 2024年第2期7卷 101-112页

作者：王亚珅方勇江昊曾园园白然中国电科智能科技研究院中国电科认知与智能技术重点实验室北京100049 中国人民解放军军事科学院军事科学信息研究中心北京100142 武汉大学电子信息学院武汉430072 中国电子科技集团有限公司电子科学研究院社会安全风险感知与防控大数据应用国家工程研究中心北京100041

生成式人工智能已经成为人工智能的最前沿技术之一,被认为是通往通用人工智能的重要阶梯。对2023年生成式人工智能的主要发展动向进行了综合评述。首先盘点生成式人工智能技术当前发展状态;其次概述生成式人工智能技术未来发展趋势;最... 详细信息

生成式人工智能已经成为人工智能的最前沿技术之一,被认为是通往通用人工智能的重要阶梯。对2023年生成式人工智能的主要发展动向进行了综合评述。首先盘点生成式人工智能技术当前发展状态;其次概述生成式人工智能技术未来发展趋势;最后探讨生成式人工智能的军事应用潜力。综述表明,多模态性和可解释性是当前生成式人工智能技术的热门方向,生成式人工智能技术的应用使强调指令理解推理的机器人、强调归纳推理的基础科学等领域不断取得突破,面向终端用户的“轻量化”和面向开发者的“开源化”势头正盛,与此同时,对生成式人工智能技术应用的监管也迫在眉睫。综述认为,深度融合多模态、建设高质量数据资源及评估手段、强调与环境自主交互、在边缘侧低成本应用部署、平衡技术发展与规制治理等,将是生成式人工智能技术在未来的重要发展方向。

关键词：生成式人工智能多模态可解释性具身智能轻量化开源监管

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Association between body mass index and in vitro fertilization/intra-cytoplasmic sperm injection outcomes: An analysis of 15,124 normal ovarian responders in China

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中华医学杂志(英文版) 2024年第7期137卷 837-845页

作者： Zheng Danlei Wang yuanyuan Chen Lixue zeng Lin Li Rong Department of Obstetrics and Gynecology Center for Reproductive Medicine Peking University Third Hospital Beijing 100191 China Research Center of Clinical Epidemiology

Background: High body mass index (BMI) results in decreased fecundity, and women with high BMI have reduced rates of clinical pregnancy and live birth inin vitro fertilization/intra-cytoplasmic sperm injection (IVF/ICSI). Meanwhile, ovarian responses show great heterogeneity in patients with a high BMI. This study aimed to analyze the effects of a high BMI on IVF/ICSI outcomes in the Chinese female with normal ovarian response.Methods: We performed a retrospective cohort study comprising 15,124 patients from the medical record system of the Reproductive Center of Peking University Third Hospital, with 3530 (23.3%) in the overweight group and 1380 (9.1%) in the obese group, who had a normal ovarian response (5-15 oocytes retrieved) and underwent fresh embryo transfer (ET) cycles from January 2017 to December 2018, followed by linked frozen-thawed embryo transfer (FET) cycles from January 2017 to December 2020. Cumulative live birth rate (CLBR) was used as the primary outcome. Furthermore, a generalized additive model was applied to visually illustrate the curvilinear relationship between BMI and the outcomes. We used a decision tree to identify the specific population where high BMI had the greatest effect on IVF/ICSI outcomes.Results: High BMI was associated with poor IVF/ICSI outcomes, both in cumulative cycles and in separate fresh ET or FET cycles. In cumulative cycles, compared with the normal weight group, obesity was correlated with a lower positive pregnancy test rate (adjusted odds ratio [aOR]: 0.809, 95% confidence interval [CI]: 0.682-0.960), lower clinical pregnancy rate (aOR: 0.766, 95% CI: 0.646-0.907), lower live birth rate (aOR: 0.706, 95% CI: 0.595-0.838), higher cesarean section rate (aOR: 2.066, 95% CI: 1.533-2.785), and higher rate of large for gestational age (aOR: 2.273, 95% CI: 1.547-3.341). In the generalized additive model, we found that CLBR declined with increasing BMI, with 24 kg/m2 as an inflection point. In the decision tree, BMI only made a difference in the population aged ≤34.5 yea

关键词： Obesity Body mass index In vitro fertilization Live birth Decision trees Normal ovarian response

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A Nonlinear Grey Bernoulli Model with Conformable Fractional-Order Accumulation and Its Application to the Gross Regional Product in the Cheng-Yu Area

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Journal of Systems Science and Information 2024年第2期12卷 245-273页

作者： Wenqing WU Xin MA Bo zeng yuanyuan ZHANG School of Science Civil Aviation Flight University of ChinaDeyang 618307China School of Mathematics and Physics Southwest University of Science and TechnologyMianyang 621010China College of Business Planning Chongqing Technology and Business UniversityChongqing 400067China

This study considers a nonlinear grey Bernoulli forecasting model with conformable fractionalorder accumulation,abbreviated as CFNGBM(1,1,λ),to study the gross regional product in the ChengYu area.The new model contains three nonlinear parameters,the power exponentγ,the conformable fractional-orderαand the background valueλ,which increase the adjustability and flexibility of the CFNGBM(1,1,λ)model.Nonlinear parameters are determined by the moth flame optimization algorithm,which minimizes the mean absolute prediction percentage error.The CFNGBM(1,1,λ)model is applied to the gross regional product of 16 cities in the Cheng-Yu area,which are Chongqing,Chengdu,Mianyang,Leshan,Zigong,Deyang,Meishan,Luzhou,Suining,Neijiang,Nanchong,Guang’an,Yibin,Ya’an,Dazhou and Ziyang.With data from 2013 to 2021,several grey models are established and results show that the new model has higher accuracy in most cases.

关键词： nonlinear grey Bernoulli model conformable fractional-order operator moth flame optimization algorithm gross regional product the Cheng-Yu area

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Heptamethine cyanines in bioorthogonal chemistry

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Chinese Chemical Letters 2024年第2期35卷 131-137页

作者： yuanyuan Liao Yuting Liang Yurou Huang Xiaoyan zeng Tian He Jun Yin National Key Laboratory of Green Pesticide International Joint Research Center for Intelligent Biosensor Technology and HealthCollege of ChemistryCentral China Normal UniversityWuhan 430079China

Due to their excellent fluorescence properties and biological function,cyanine dyes have been widely applied in biological imaging.Heptamethine cyanine(Cy7)dyes,as a type of classic near-infrared(NIR)fluorescent dyes,are considered as one of the effective fluorescent tools in the living organisms due to their good biocompatibility and very low background interference.Bioorthogonal reactions performed in living cells and tissues have developed by leaps and bounds in recent years.The NIR fluorescent labeling technique involving cyanine has attracted widespread attention.This review summarizes their recent application in the field of bioorthogonal imaging,mainly concluding Cy7-type dyes,labeling strategy,bioimaging application,etc.We expect this work can provide some helps for the studies of NIR bioorthogonal reaction in vivo.

关键词： Fluorescent probes Heptamethine cyanines Bioorthogonal reaction Imaging Cell

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