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Effects of Acupuncture on Alzheimer’s Disease: Evidence from Neuroimaging Studies

Chinese Journal of Integrative Medicine 2019年第8期25卷 631-640页

作者： YU chao-chao ma chao-yang WANG Hua KONG Li-hong ZHAO Yan SHEN Feng WU Miao College of Acupuncture and Orthopedics Hubei University of Chinese Medicine Wuhan (430061)China Department of Rehabilitation Wuhan Central Hospital Affiliated to Tongji Medical College Huazhong University of Science and Technology Wuhan (430014) China Hubei Provincial Collaborative Innovation Center of Preventive Treatment by Acupuncture and Moxibustion Hubei University of Chinese Medicine Wuhan (430061)China Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan (430061)China Hubei Province Academy of Traditional Chinese Medicine Wuhan (430074)China

As the worldwide population ages, the prevalence of Alzheimer's disease (AD) increases. However, the results of promising medications have been unsatisfactory. Chinese acupuncture has a long history of treating dementia, but lack of evidence from well-designed randomized controlled trials that validate its efficacy and safety, as well as its lack of clear underlying mechanisms, contribute to its limited application in clinical practice. In recent years, brain imaging technologies, such as functional magnetic resonance imaging and positron emission tomography, have been used to assess brain responses to acupuncture in a dynamic, visual, and objective way. These techniques are frequently used to explore neurological mechanisms of responses to acupuncture in AD and provide neuroimaging evidence as well as starting points to elucidate the possible mechanisms. This review summarizes the existing brain imaging evidence that explains the effects of acupuncture for AD and analyzes brain responses to acupuncture at cognitive-related acupoints [Baihui (GV 20), Shenmen (HT 7), Zusanli (ST 36), Neiguan (PC 6), and Taixi (KI 3)] from perspectives of acupoint specificity and acupoint combinations. Key issues and directions to consider in future studies are also put forward. This review should deepen our understanding of how brain imaging studies can be used to explore the underlying mechanisms of acupuncture in AD.

关键词： acupuncture Alzheimer's disease brain response acupoint specificity neuroimaging

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Development and validation of a preoperative prediction model for all-cause mortality in patients following abdominal aortic aneurysm repair

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中华医学杂志(英文版) 2024年第10期137卷 1255-1257页

作者： Li Tan Li Guangxiao Wu Yijun yang chao yang Jun Sun Liping ma Chunyan Department of Cardiovascular Ultrasound The First Hospital of China Medical University Shenyang Liaoning China Clinical Medical Research Center of Imaging in Liaoning Province The First Hospital of China Medical University Shenyang Liaoning China Department of Medical Record management Center The First Hospital of China Medical University Shenyang Liaoning China Tumor Etiology and Screening Department of Cancer Institute and General Surgery The First Hospital of China Medical University Shenyang Liaoning China Trauma Center and Department of Burns The First Hospital of China Medical University Shenyang Liaoning China

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease. Surgical intervention is typically recommended when the maximum AAA diameter reaches a threshold of 5.5ficm for men and 5.0ficm for women, or when an AAA experiences a rapid expansion of greater than 1.0ficm/year.[1] Survival after AAA repair is influenced by various factors, and the identification of independent prognostic factors and the development of accurate prediction models are crucial for optimal treatment planning, counseling, and postoperative follow-up. Although several scoring systems with varying degrees of accuracy have been developed to predict the risk of mortality after intervention for patients with ruptured AAA, conflicting results regarding their performance have been published.[2,3] Ideally, prediction models should be user-friendly and widely applicable, providing quick and reliable estimates of outcomes based on only a few readily available preoperative parameters. Unfortunately, the number of clinical models assisting clinicians in estimating the long-term mortality of general AAA patients after aneurysm repair is limited, and there is a lack of scores focusing on the Chinese population. Therefore, the objectives of the present study were to explore potential biomarkers affecting the long-term mortality risk of patients who underwent AAA repair and to establish an easy-to-use and practical preoperative risk prediction model for all-cause mortality after AAA repair in Chinese population. This model might help stratify AAA patients into different groups according to their predicted life expectancy, informing decision-making, and implementing medical intervention in clinical trials.

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Intelligent feedforward gust alleviation based on neural network

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Chinese Journal of Aeronautics 2024年第3期37卷 116-132页

作者： Yitao ZHOU Zhigang WU chao yang School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China

This paper proposes a neural network-based intelligent feedforward gust alleviation framework,which includes a neural network identification model and a neural network controller.A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight.A neural network identification model is first trained to accurately predict the aircraft’s output.Then,based on the output of the identification model and the collected flight data,the parameters of the time-delay neural network controller are obtained through a learning process.The simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust excitation.For example,when the aircraft is 40000 kg and the flight speed is 0.81ma,the controller achieves a 67.82%reduction in wingtip acceleration and a 35.90%reduction in center of mass acceleration under continuous turbulence excitation.When considering the measurement uncertainties,such as noise existing in the collected data,the trained controller can still achieve an acceptable gust alleviation effect.Finally,considering a flight in which the aircraft mass is constantly changing,the intelligent controller,which continuously learns from new flight data,maintains a good gust alleviation effect throughout the flight.

关键词： Gust alleviation Intelligent control Feedforward control Neural networks Time-varying aircraft

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Preoperative neutrophil-to-lymphocyte ratio predicts symptomatic anastomotic leakage in elderly colon cancer patients: Multicenter propensity score-matched analysis

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World Journal of Gastrointestinal Surgery 2024年第2期16卷 438-450页

作者： chao-yang Wang Xiao-Long Li Xiao-Long ma Xiong-Fei yang Yong-Yong Liu Yong-Jiang Yu Department of Clinical Medicine The First Clinical Medical College of Lanzhou UniversityLanzhou 730000Gansu ProvinceChina Department of Gastrointestinal Surgery First Hospital of Lanzhou UniversityLanzhou 730000Gansu ProvinceChina Department of Clinical Medicine Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina Department of Anorectology Gansu Provincial People’s HospitalLanzhou 730000Gansu ProvinceChina Department of General Surgery The Second Hospital Affiliated to Lanzhou UniversityLanzhou 730000Gansu ProvinceChina

BACKGROUND The neutrophil-to-lymphocyte ratio(NLR),a composite inflammatory biomarker,is associated with the prognosis in patients with colorectal tumors.However,whether the NLR can be used as a predictor of symptomatic postoperative ana-stomotic leakage(AL)in elderly patients with colon cancer is unclear.AIM To assess the role of the NLR in predicting the occurrence of symptomatic AL after surgery in elderly patients with colon cancer.METHODS Data from elderly colon cancer patients who underwent elective radical colectomy with anastomosis at three centers between 2018 and 2022 were retrospectively analyzed.Receiver operating characteristic curve analysis was performed to determine the best predictive cutoff value for the NLR.Twenty-two covariates were matched using a 1:1 propensity score matching method,and univariate and multivariate logistic regression analyses were used to determine risk factors for the development of postoperative AL.RESULTS Of the 577 patients included,36(6.2%)had symptomatic AL.The optimal cutoff value of the NLR for predicting AL was 2.66.After propensity score matching,the incidence of AL was significantly greater in the≥2.66 NLR subgroup than in thematic AL.CONCLUSION A preoperative NLR≥2.66 and blood transfusion intraoperatively and within 2 d postoperatively are associated with a higher incidence of postoperative symptomatic AL in elderly patients with colon cancer.The preoperative NLR

关键词： Colon cancer Anastomotic leak Neutrophil-to-lymphocyte ratio Propensity score-matched

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Micelle-directed self-assembly of single-crystal-like mesoporous stoichiometric oxides for high-performance lithium storage

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National Science Review 2024年第4期11卷 195-205页

作者： Yanhua Wan Changyao Wang Xingmiao Zhang yang Yin Mengmeng Liu Bing ma Linlin Duan Yuzhu ma Wei Zhang Changlin Zheng Dongliang chao Fei Wang Yongyao Xia Wei Li Department of Chemistry Laboratory of Advanced Materials Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials State Key Laboratory of Molecular Engineering of Polymers College of Chemistry and Materials ScienceFudan University State Key Laboratory of Surface Physics and Department of Physics Fudan University

Due to their uncontrollable assembly and crystallization process,the synthesis of mesoporous metal oxide single crystals remains a formidable challenge.Herein,we report the synthesis of single-crystal-like mesoporous Li2TiSiO5by using soft micelles as templates.The key lies in the atomic-scale self-assembly and step-crystallization processes,which ensure the formation of single-crystal-like mesoporous Li2TiSiO5microparticles via an oriented attachment growth mechanism under the confinement of an in-situ formed carbon matrix.The mesoporous Li2TiSiO5anode achieves a superior rate capability(148 mah g-1at5.0 A g-1) and outstanding long-term cycling stability(138 mah g-1after 3000 cycles at 2.0 A g-1) for lithium storage as a result of the ultrafast Li+diffusion caused by penetrating mesochannels and nanosized crystal frameworks（5-10 nm）.In comparison,bulk Li2TiSiO5exhibits poor rate capability and cycle performance due to micron-scale diffusion lengths.This method is very simple and reproducible,heralding a new way of designing and synthesizing mesoporous single crystals with controllable frameworks and chemical functionalities.

关键词： mesoporous materials micelles single-crystal-like Li2TiSiO5 lithium-ion batteries

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Input-to-State Stability for Impulsive Switched Singular Systems with Mismatched Disturbances

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Journal of Systems Science & Complexity 2024年第3期37卷 924-946页

作者： yang Anqing ma Shuping School of Information Engineering Shandong Management UniversityJinan 250357China School of mathematics Shandong UniversityJinan 250100China

This work investigates the input-to-state stability(ISS)problem for impulsive switched singular systems(ISSSs)with mismatched disturbances.In this paper,‘disturbance’is a general concept that includes model uncertainty,unknown system dynamic,external disturbance,etc.The modified uncertainty and disturbance estimator(UDE)-based control method is presented for singular systems and ISSSs,a virtual control is introduced to offset the effects of mismatched disturbances.On the basis of a discontinuous multiple Lyapunov functional and admissible edge-dependent average dwell time(AED-ADT)method,several sufficient conditions in terms of linear matrix inequalities(LMIs)are obtained to ensure that the closed-loop systems are regular,impulse-free and ISS.Finally,two examples are given to demonstrate the effectiveness of the proposed results.

关键词： Impulsive switched singular systems input-to-state stability UDE-based control

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Mitigate noisy data for smart IoT via GAN based machine unlearning

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Science China(Information Sciences) 2024年第3期67卷 62-78页

作者： Zhuo ma Yilong yang yang LIU Xinjing LIU Jianfeng ma School of Cyber Engineering Xidian University State Key Laboratory of Integrated Services Networks (ISN)

With the development of IoT applications, machine learning dramatically improves the utility of variable IoT systems such as autonomous driving. Although the pretrain-finetune framework can cope well with data heterogeneity in complex IoT scenarios, the data collected by sensors often contain unexpected noisy data, e.g., out-of-distribution(OOD) data, which leads to the reduced performance of fine-tuned models. To resolve the problem, this paper proposes Mu GAN, a method that can mitigate the side-effect of OOD data via the generative adversarial network(GAN)-based machine unlearning. Mu GAN follows a straightforward but effective idea to mitigate the performance loss caused by OOD data, i.e., “flashbacking” the model to the condition where OOD data are excluded from model training. To achieve the goal, we design an adversarial game, where a discriminator is trained to identify whether a sample belongs to the training set by observing the confidence score. Meanwhile, a generator(i.e., the target model) is updated to fool the discriminator into believing that the OOD data are not included in the training set but others do. The experimental results show that benefiting from the high unlearning rate(more than 90%) and retention rate(99%), Mu GAN succeeds in lowering the model performance degradation caused by OOD data from 5.88% to 0.8%.

关键词： machine unlearning generative adversarial network out of distribution data Internet of Thing neural network

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GNAS mutations suppress cell invasion by activating MEG3 in growth hormone–secreting pituitary adenoma

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Oncology Research 2024年第6期32卷 1079-1091页

作者： chao TANG CHUNYU ZHONG JUNHAO ZHU FENG YUAN JIN yang YONG XU CHIYUAN ma Department of Neurosurgery Affiliated Jinling HospitalMedical School of Nanjing UniversityNanjing210002China Department of Neurosurgery Children’s Hospital of Nanjing Medical UniversityNanjing210019China Affiliated Eye Hospital Nanjing Medical UniversityNanjing210029China Department of Neurosurgery Jinling HospitalSouthern Medical UniversityNanjing210002China Department of Neurosurgery Jinling HospitalNanjing Medical UniversityNanjing210002China

Approximately 30%–40%of growth hormone–secreting pituitary adenomas(GHPAs)harbor somatic activating mutations in GNAS(αsubunit of stimulatory G protein).Mutations in GNAS are associated with clinical features of smaller and less invasive tumors.However,the role of GNAS mutations in the invasiveness of GHPAs is unclear.GNAS mutations were detected in GHPAs using a standard polymerase chain reaction(PCR)sequencing procedure.The expression of mutation-associated maternally expressed gene 3(MEG3)was evaluated with RT-qPCR.MEG3 was manipulated in GH3 cells using a lentiviral expression system.Cell invasion ability was measured using a Transwell assay,and epithelial–mesenchymal transition(EMT)-associated proteins were quantified by immunofluorescence and western blotting.Finally,a tumor cell xenograft mouse model was used to verify the effect of MEG3 on tumor growth and invasiveness.The invasiveness of GHPAs was significantly decreased in mice with mutated GNAS compared with that in mice with wild-type GNAS.Consistently,the invasiveness of mutant GNASexpressing GH3 cells decreased.MEG3 is uniquely expressed at high levels in GHPAs harboring mutated GNAS.Accordingly,MEG3 upregulation inhibited tumor cell invasion,and conversely,MEG3 downregulation increased tumor cell invasion.Mechanistically,GNAS mutations inhibit EMT in GHPAs.MEG3 in mutated GNAS cells prevented cell invasion through the inactivation of the Wnt/β-catenin signaling pathway,which was further validated in vivo.Our data suggest that GNAS mutations may suppress cell invasion in GHPAs by regulating EMT through the activation of the MEG3/Wnt/β-catenin signaling pathway.

关键词： GHPAs GNAS mutation MEG3 β-catenin EMT

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GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

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Global Energy Interconnection 2024年第2期7卷 117-129页

作者： Xiaoyun Deng Yongdong Chen Dongchuan Fan Youbo Liu chao ma College of Electrical Engineering Sichuan UniversityChengdu 610065P.R.China State Grid Sichuan Comprehensive Energy Service Co.Ltd. Chengdu 610072P.R.China

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

关键词： Residential virtual power plant Residential distributed energy resource Constrained soft actor-critic Fully distributed scheduling strategy

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Structural evolution and high-temperature sensing performance of polymer-derived SiAlBCN ceramics

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Journal of Advanced Ceramics 2024年第4期13卷 478-485页

作者： chao ma Kun Liu Pengfei Shao Daoyang Han Kang Wang Mengmeng yang Rui Zhao Hailong Wang Rui Zhang Gang Shao School of materials Science and Engineering Zhengzhou UniversityZhengzhou 450001China Zhongyuan Critical Metal Laboratory Zhengzhou UniversityZhengzhou 450001China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentZhengzhou UniversityZhengzhou 450002China

In situ temperature monitoring has become extremely imperative in high-temperature harsh environments and polymer-derived ceramics(PDCs)as sensing materials have attracted great attention.However,the stability and oxidation/corrosion resistance of PDCs cannot be simultaneously achieved at the moment,limiting their practical application.Herein,polymer-derived SiAlBCN ceramics were synthesized via polymer conversion method under different pyrolysis temperatures.Their microstructure evolution,high temperature sensing properties,and stability were investigated in detail.The results show that the amorphous SiAlBCN phase grows more orderly and the size of the free carbon phase enlarges with the increasing temperature.The defect concentration displays a decreasing tendency.Concurrently,the SiAlBCN ceramics as sensing materials exhibit a good temperature-resistance property from roo temperature to 1100℃.The fabricated SiAlBCN temperature sensor possesses excellent stability,repeatability,and accuracy.Moreover,SiAlBCN ceramics exhibit distinguished oxidation/corrosion resistance after 100 h treatment at 1200℃in a water/oxygen environment,which is attributed to their low corrosive rate constant(0.57 mg/(cm^(2)·h))and oxidative rate constant(3.43 mg^(2)/(cm^(4)·h)).Therefore,polymer-derived SiAlBCN ceramics as sensing materials,which possess outstanding stability and oxidation/corrosion resistance,have great potential for in-situ monitoring of extreme environmental temperatures in the future.

关键词： polymer-derived SiAlBCN ceramics temperature sensor harsh environments oxidation and corrosion resistance

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