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Serum neuron-specific enolase:A promising biomarker of silicosis

World Journal of Clinical Cases 2021年第5期9卷 1016-1025页

作者： Hong-bo huang Jun-Ling huang Xiao-Ting Xu kun-bo huang Yi-Jian Lin Jie-Bin Lin Xi-Bin Zhuang Department of Pulmonary and Critical Care Medicine Quanzhou First Hospital Affiliated to Fujian Medical UniversityQuanzhou 362000Fujian ProvinceChina Department of Clinical Laboratory Quanzhou First Hospital Affiliated to Fujian Medical UniversityQuanzhou 362000Fujian ProvinceChina Department of Internal Medicine Infectious Disease Hospital of QuanzhouQuanzhou 362000Fujian ProvinceChina

BACKGROUND Silicosis is a type of chronic pulmonary fibrosis caused by long-term inhalation of silica dust *** has been no ideal biomarker for the diagnosis and differential diagnosis of silicosis until *** have found that elevated neuron-specific enolase(NSE)concentration in the serum of silicosis patients is helpful for diagnosis and severity assessment of the ***,the number of cases in these studies was not enough to arouse *** To investigate the clinical significance of serum NSE in the diagnosis and staging of *** From January 2017 to June 2019,326 cases of silicosis confirmed in Quanzhou First Hospital Affiliated to Fujian Medical University were included in the silicosis group.A total of 328 healthy individuals or medical patients without silicosis were included in the control *** NSE concentrations of all subjects were determined by electrochemical *** There were no significant differences in sex,age,smoking index and complications between the silicosis and control *** mean serum NSE concentration was 26.57±20.95 ng/mL in the silicosis group and 12.42±2.68 ng/mL in the control *** difference between the two groups was significant(U=15187,P=0.000).Among the 326 patients with silicosis,103 had stage I silicosis,and the mean serum NSE concentration was 15.55±6.23 ng/*** mean serum NSE concentration was 21.85±12.05 ng/mL in 70 patients with stage II *** mean serum NSE concentration was 36.14±25.72 ng/mL in 153 patients with stage III ***-Wallis H test suggested that the difference in serum NSE concentration in silicosis patients in the three groups was significant(H=130.196,P=0.000).Receiver operating characteristic curve analysis indicated that the area under the curve was 0.858(95%confidence interval:0.828-0.888;P=0.000).When the NSE concentration was 15.82 ng/mL,the Jorden index was the largest,the sensitivity was 72%,and the specificity wa

关键词： Silicosis Neuron-specific enolase Receiver operating characteristic curve Disease stage Biomarker Diagnosis

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang bo Li Institute of Biomechanics and Medical Engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University Institute of Nuclear and New Energy Technology Tsinghua University Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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Deep learning identification of novel autophagic protein-protein interactions and experimental validation of Beclin 2-Ubiquilin 1 axis in triple-negative breast cancer

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Oncology Research 2025年第1期33卷 67-81页

作者： XIANG LI WENKE JIN LIFENG WU HUAN WANG XIN XIE WEI huang bo LIU State Key Laboratory of Southwestern Chinese Medicine Resources School of Pharmacy and College of Medical TechnologyChengdu University of Traditional Chinese MedicineChengdu611137China Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041China

Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional *** is an important process for maintaining cellular homeostasis,and there are currently autophagy biomarkers that play an effective role in the clinical treatment of *** contrast to targeting protein activity,intervention with proteinprotein interaction(PPI)can avoid unrelated crosstalk and regulate the autophagy process with minimal interference ***:Here,we employed Naive Bayes,Decision Tree,and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNBC,aiming to uncover novel therapeutic ***,the candidate proteins interacting with Beclin 2 were initially screened in MDA-MB-231 cells using Beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay,and the interaction relationship was verified by molecular docking and CO-IP experiments after *** formation,cellular immunofluorescence,cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT)tests were used to predict the clinical therapeutic effects of manipulating candidate ***:By developing three PPI classification models and analyzing over 13,000 datasets,we identified 3733 previously unknown autophagy-related *** network analysis revealed the central role of Beclin 2 in autophagy regulation,uncovering its interactions with 39 newly identified ***,the CO-IP studies identified the substantial interaction between Beclin 2 and Ubiquilin 1,which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay ***,in vitro investigations showed that overexpressing Beclin 2 increased Ubiquilin 1,promoted autophagy-dependent cell death,and inhibited proliferation and metastasis in MDA-MB-231 ***:This study not

关键词： Triple-negative breast cancer(TNBC) Autophagy Protein-protein interactions(PPI) Artificial intelligence(AI) Beclin 2 Ubiquilin 1

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The potential mechanism and clinical application value of remote ischemic conditioning in stroke

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Neural Regeneration Research 2025年第6期20卷 1613-1627页

作者： Yajun Zhu Xiaoguo Li Xingwei Lei Liuyang Tang Daochen Wen bo Zeng Xiaofeng Zhang Zichao huang Zongduo Guo The First Affiliated Hospital of Chongqing Medical University ChongqingChina

Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may be related to neuroinflammation, cellular immunity, apoptosis, and autophagy, the exact underlying molecular mechanisms are unclear. This review summarizes the current status of different types of remote ischemic conditioning methods in animal and clinical studies and analyzes their commonalities and differences in neuroprotective mechanisms and signaling pathways. Remote ischemic conditioning has emerged as a potential therapeutic approach for improving stroke-induced brain injury owing to its simplicity, non-invasiveness, safety, and patient tolerability. Different forms of remote ischemic conditioning exhibit distinct intervention patterns, timing, and application range. Mechanistically, remote ischemic conditioning can exert neuroprotective effects by activating the Notch1/phosphatidylinositol 3-kinase/Akt signaling pathway, improving cerebral perfusion, suppressing neuroinflammation, inhibiting cell apoptosis, activating autophagy, and promoting neural regeneration. While remote ischemic conditioning has shown potential in improving stroke outcomes, its full clinical translation has not yet been achieved.

关键词： Akt apoptosis autophagy cerebral perfusion cerebral vascular stenosis clinical transformation hemorrhagic stroke ischemic stroke neuroinflammation neuroprotection Notch1 PI3K remote ischemic conditioning stroke

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Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 18-23页

作者： Jiarui Cai bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan huang Peishu Zong Collaborative Innovation Center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina China Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(Dbo)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with Dbo show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original Dbo,the R and RPD of models with the improved Dbo increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

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Unraveling the impacts of intermolecular interaction on morphology evolution for highly efficient organic solar cells and modules

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Science China Chemistry 2025年第1期68卷 264-272页

作者： Xueqing Ma Yingying Cheng Yuqiang Liu Xinming Zheng Guangliu Ran Hongxiang Li Xinyue Cui Andong Zhang Wenkai Zhang Pei Cheng Wenchao huang Zhishan bo Beijing Key Laboratory of Energy Conversion and Storage Materials College of Chemistry Beijing Normal University College of Textiles and Clothing State Key Laboratory of Bio-fibers and Eco-textiles Qingdao University Key State Laboratory of Advanced Technology for Materials Synthesis and Processing School of Materials Science and EngineeringWuhan University of Technology Department of Physics and Applied Optics Beijing Area Major Laboratory Center for Advanced Quantum Studies Beijing Normal University State Key Laboratory of Polymer Materials Engineering College of Polymer Science and Engineering Sichuan University

Understanding the effect of intermolecular interaction on the growth dynamic of active layers is critical for advancing organic solar cells(OSCs). However, the diverse structure of donors and acceptors makes the research challenging. Additives with customizable structures and properties could simplify this complexity. Herein, we meticulously tailor two additives of 3,4-ethylenedioxythiophene(EDOT) and 2,5-dibromo-3,4-ethylenedioxythiophene(DBEDOT), possessing distinct intermolecular interaction features to elaborate the inherent relationship. It is found that varied interaction strengths can alter film formation processes. The enhanced intermolecular interaction between the DBEDOT and non-fullerene acceptor BTP-e C9-4F results in pre-aggregation and longer crystallization duration of BTP-e C9-4F, facilitating the formation of films with compact molecular packing and decent phase separation. Thus, exciton dissociation and charge transport become more efficient. Finally, devices processed with DBEDOT exhibit a remarkable power conversion efficiency of 19.35% in small-area OSCs and 14.11% in bladecoated 5 cm × 5 cm organic solar mini-modules. Especially, OSCs can maintain 80% of their initial efficiency after continuous annealing at 85 °C for over 2,100 h.

关键词： organic solar cell non-fullerene acceptor film growth molecular packing intermolecular interaction

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Graphene oxide wrapped-silica microspheres humidity sensor with fast response/recovery, high sensitivity, and selectivity for pig respiratory rate monitoring

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Science China Technological Sciences 2025年

作者： Jing LI bo LI Ziqi LIAO Yunhui DUAN Xinglan FU Yuehua huang Guanglin LI College of Engineering and Technology Southwest University Modern Agricultural Equipment Research Institute Xihua University

Respiratory rate monitoring is of great significance in pig healthy breeding for the early detection and timely prevention of respiratory diseases. Humidity sensing is an important noncontact method to monitor pig respiration, which does not cause direct harm to pigs and meets the requirements of animal welfare. This paper proposes a humidity sensor based on graphene oxide wrapped-silica microspheres （GO@SiO2）. First, the sensor shows fast response/recovery performances of0.24/0.73 s. Experiments demonstrate that the wrapped microsphere structure can inhibit water aggregation. It is speculated that the wrapped microsphere structure can inhibit the stacking of GO, and its curved surface can promote the evaporation of water molecules. Second, the sensor shows a high sensitivity of 1.2%/%RH–4.2%/%RH in 11%–95%RH range. This can be attributed to different sensing mechanisms, from hopping transport to increasing ion transport, leading to a significant change in the sensor’s resistance. Third, the sensor shows selectivity to water molecules due to the hydrophilic characteristic of GO and the significant increase in conductivity by ion transport. Finally, the sensor is applied to realize the respiratory rate monitoring of adult pigs and piglets.

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Moment Redistribution Effect of the Continuous Glass Fiber Reinforced Polymer-Concrete Composite Slabs Based on Static Loading Experiment

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Structural Durability & Health Monitoring 2025年第1期19卷 105-123页

作者： Zhao-Jun Zhang Wen-Wei Wang Jing-Shui Zhen bo-Cheng Li De-Cheng Cai Yang-Yang Du Hui huang Offshore Oil Engineering Co.Ltd. Tianjin300461China Department of Bridge Engineering School of TransportationSoutheast UniversityNanjing211189China

This study aimed to investigate the moment redistribution in continuous glass fiber reinforced polymer(GFRP)-concrete composite slabs caused by concrete cracking and steel bar yielding in the negative bending moment *** experimental bending moment redistribution test was conducted on continuous GFRP-concrete composite slabs,and a calculation method based on the conjugate beam method was *** composite slabs were formed by combining GFRP profiles with a concrete layer and supported on steel beams to create two-span continuous composite slab *** methods,epoxy resin bonding,and stud connection,were used to connect the composite slabs with the steel *** experimental findings showed that the specimen connected with epoxy resin exhibited two moments redistribution phenomena during the loading process:concrete cracking and steel bar yielding at the internal *** contrast,the composite slab connected with steel beams by studs exhibited only one-moment redistribution phenomenon throughout the loading *** the concrete at the internal support cracked,the bending moment decreased in the internal support section and increased in the midspan *** the steel bars yielded,the bending moment further decreased in the internal support section and increased in the mid-span *** GFRP profiles do not experience cracking,there was no significant decrease in the bending moment of the mid-span *** test specimens experienced compressive failure of concrete at the mid-span *** results showed good agreement between the calculated and experimental values of bending moments in the mid-span section and internal support *** proposed model can effectively predict the moment redistribution behavior of continuous GFRP-concrete composite slabs.

关键词： Moment redistribution GFRP-concrete composite slabs bending moment experimental study analysis model

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan huang He JIANG Weixing JI Yanjie JIANG Yanyan JIANG bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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淤地坝溃坝风险性评估综述

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灾害学 2025年第1期40卷 123-128,134页

作者：黄波张耀南中国科学院西北生态环境资源研究院兰州甘肃730000 中国科学院大学北京1000493 国家冰川冻土沙漠科学数据中心兰州甘肃730000 甘肃省科学数据总中心兰州甘肃730000

淤地坝作为一种关键的防灾减灾设施,对于粮食生产、水资源保持和洪水控制等方面发挥着至关重要的作用。上世纪六七十年代修建的大部分淤地坝面临淤满失效问题,突发的暴雨、地震等自然灾害将极大概率引起溃决,从而导致引发更为严重的次... 详细信息

淤地坝作为一种关键的防灾减灾设施,对于粮食生产、水资源保持和洪水控制等方面发挥着至关重要的作用。上世纪六七十年代修建的大部分淤地坝面临淤满失效问题,突发的暴雨、地震等自然灾害将极大概率引起溃决,从而导致引发更为严重的次生灾害。该文通过梳理黄土高原淤地坝溃坝风险性评价文献,总结了现有淤地坝溃坝风险性评价方法。结果表明,传统淤地坝风险性评价方法对影响因素数据的依赖程度很高;选取评价因子及其权重赋予存在较强的主观经验性;缺乏大范围的风险评估。因此,该文建议整合数据驱动模型的研究思路,利用多源数据,区域尺度上开展淤地坝溃坝风险性评价,为相关部门提供更可靠的决策支持,及具有提高防灾减灾能力实践意义。

关键词：淤地坝溃坝数据工程风险性评价

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