目的分析2008—2024年老年性肌少症与线粒体相关性研究的现状、热点及发展趋势,为该领域的后续研究提供参考。方法检索2008年1月1日至2024年12月31日Web of Science核心合集数据库收录的老年性肌少症与线粒体相关性研究的文献,使用R 4....目的分析2008—2024年老年性肌少症与线粒体相关性研究的现状、热点及发展趋势,为该领域的后续研究提供参考。方法检索2008年1月1日至2024年12月31日Web of Science核心合集数据库收录的老年性肌少症与线粒体相关性研究的文献,使用R 4.2.0软件的Bibliometrix包对发文国家、合作网络、作者、机构、期刊、高被引文献、关键词和文献被引频次进行定量和可视化分析,并运用H指数分析作者的学术影响力。结果共纳入1219篇文献,2008—2024年发文量总体呈上升趋势。累计发文量排名前三位的国家分别是美国、中国和意大利;发文量排名前三位的期刊分别为Journal of Cachexia,Sarcopenia and Muscle、International Journal of Molecular Sciences和Experimental Gerontology;H指数排名前六位的作者分别为Marzettie E、Calvani R、Picca A、Van Remmen H、Leeuwenbugh C和Bernabel R;被引频次最高的文献是“Sarcopenia:agingrelated loss of muscle mass and function”;出现频次排名前五的关键词分别为skeletalmuscle、sarcopenia、oxidative stress、exercise和expression。结论老年性肌少症与线粒体相关性研究领域呈现良好的发展态势。未来需加强跨国家、跨机构和跨学科合作,可重点关注线粒体融合蛋白等对线粒体功能的影响,以及饮食和运动对老年性肌少症的干预作用等方面的探索。展开更多
Regulation of apoptosis represents a key parameter in all living organisms.In this paper,an input-induced logic-gated modular nanocalculator is designed to regulate cancer cell apoptosis by programmatically combining ...Regulation of apoptosis represents a key parameter in all living organisms.In this paper,an input-induced logic-gated modular nanocalculator is designed to regulate cancer cell apoptosis by programmatically combining and connecting logic gate modules with different functions.Via rational design of the various logic gate modules of the nanocalculator,different apoptosis related operations including cancer cell targeting,apoptosis induction,and apoptosis monitoring could be performed.Importantly,each of these logic gate modules could independently perform apoptosis related YES logic operations when ran separately.After combining each YES logic gate module into a logic circuit and connecting it to the GO scaffold to construct a logic-gated nanocalculator,the input-induced logic-gated modular nanocalculator could selectively enter cancer cells and control the drug release to logically apoptosis(output),by performing AND logic gate operations when inputs(nucleolin and H^(+)) were included at the same time.Moreover,evidence suggests that these efficient logical calculations proceed in cancer cell apoptosis regulation without the general limiations of lithography in nanotechnology.As such,this work provides a new vision for the construction of a logic-gated modular nanocalculator with logical calculation proficiency potentially useful in cancer therapy and the regulation of life.展开更多
In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent ne...In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent neural networks,etc.,have been limited in research on γ spectrum analysis because of their inherent mathematical mechanisms.It is difficult to make progress in terms of training data requirements and prediction accuracy.In contrast to traditional network paradigms,network models based on the transformer structure have the characteristics of parallel computing,position encoding,and deep stacking,which have enabled good performance in natural language processing tasks in recent years.Therefore,in this paper,a transformer-based neural network (TBNN) model is proposed to achieve nuclide identification for the first time.First,the Geant4 program was used to generate the basic single-nuclide energy spectrum through Monte Carlo simulations.A multi-nuclide energy spectrum database was established for neural network training using random matrices of γ-ray energy,activity,and noise.Based on the encoder–decoder structure,a network topology based on the transformer was built,transforming the 1024-channel energy spectrum data into a 32×32 energy spectrum sequence as the model input.Through experiments and adjustments of model parameters,including the learning rate of the TBNN model,number of attention heads,and number of network stacking layers,the overall recognition rate reached 98.7%.Additionally,this database was used for training AI models such as back-propagation networks,convolutional neural networks,residual networks,and long shortterm memory neural networks,with overall recognition rates of 92.8%,95.3%,96.3%,and 96.6%,respectively.This indicates that the TBNN model exhibited better nuclide identification among these AI models,providing an important reference and theoretical basis for the practical application of transformers in the qualitative and quantitative analysis of the γ spectrum.展开更多
文摘目的分析2008—2024年老年性肌少症与线粒体相关性研究的现状、热点及发展趋势,为该领域的后续研究提供参考。方法检索2008年1月1日至2024年12月31日Web of Science核心合集数据库收录的老年性肌少症与线粒体相关性研究的文献,使用R 4.2.0软件的Bibliometrix包对发文国家、合作网络、作者、机构、期刊、高被引文献、关键词和文献被引频次进行定量和可视化分析,并运用H指数分析作者的学术影响力。结果共纳入1219篇文献,2008—2024年发文量总体呈上升趋势。累计发文量排名前三位的国家分别是美国、中国和意大利;发文量排名前三位的期刊分别为Journal of Cachexia,Sarcopenia and Muscle、International Journal of Molecular Sciences和Experimental Gerontology;H指数排名前六位的作者分别为Marzettie E、Calvani R、Picca A、Van Remmen H、Leeuwenbugh C和Bernabel R;被引频次最高的文献是“Sarcopenia:agingrelated loss of muscle mass and function”;出现频次排名前五的关键词分别为skeletalmuscle、sarcopenia、oxidative stress、exercise和expression。结论老年性肌少症与线粒体相关性研究领域呈现良好的发展态势。未来需加强跨国家、跨机构和跨学科合作,可重点关注线粒体融合蛋白等对线粒体功能的影响,以及饮食和运动对老年性肌少症的干预作用等方面的探索。
基金financially supported by the National Natural Science Foundation of China (NSFC,Nos.22134005 and 22074124)Chongqing Talents Program for Outstanding Scientists (No.cstc2021ycjh-bgzxm0178)+1 种基金Natural Science Foundation of Chongqing (No.CSTB2022NSCQ-MSX0521)the Chongqing Graduate Student Scientific Research Innovation Project (No.CYB21119)。
文摘Regulation of apoptosis represents a key parameter in all living organisms.In this paper,an input-induced logic-gated modular nanocalculator is designed to regulate cancer cell apoptosis by programmatically combining and connecting logic gate modules with different functions.Via rational design of the various logic gate modules of the nanocalculator,different apoptosis related operations including cancer cell targeting,apoptosis induction,and apoptosis monitoring could be performed.Importantly,each of these logic gate modules could independently perform apoptosis related YES logic operations when ran separately.After combining each YES logic gate module into a logic circuit and connecting it to the GO scaffold to construct a logic-gated nanocalculator,the input-induced logic-gated modular nanocalculator could selectively enter cancer cells and control the drug release to logically apoptosis(output),by performing AND logic gate operations when inputs(nucleolin and H^(+)) were included at the same time.Moreover,evidence suggests that these efficient logical calculations proceed in cancer cell apoptosis regulation without the general limiations of lithography in nanotechnology.As such,this work provides a new vision for the construction of a logic-gated modular nanocalculator with logical calculation proficiency potentially useful in cancer therapy and the regulation of life.
基金supported by the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province(Nos.2024NSFSC0422,23NSFSCC0116)Nuclear Energy Development Project(No.[2021]-88).
文摘In current neural network algorithms for nuclide identification in high-background,poor-resolution detectors,traditional network paradigms including back-propagation networks,convolutional neural networks,recurrent neural networks,etc.,have been limited in research on γ spectrum analysis because of their inherent mathematical mechanisms.It is difficult to make progress in terms of training data requirements and prediction accuracy.In contrast to traditional network paradigms,network models based on the transformer structure have the characteristics of parallel computing,position encoding,and deep stacking,which have enabled good performance in natural language processing tasks in recent years.Therefore,in this paper,a transformer-based neural network (TBNN) model is proposed to achieve nuclide identification for the first time.First,the Geant4 program was used to generate the basic single-nuclide energy spectrum through Monte Carlo simulations.A multi-nuclide energy spectrum database was established for neural network training using random matrices of γ-ray energy,activity,and noise.Based on the encoder–decoder structure,a network topology based on the transformer was built,transforming the 1024-channel energy spectrum data into a 32×32 energy spectrum sequence as the model input.Through experiments and adjustments of model parameters,including the learning rate of the TBNN model,number of attention heads,and number of network stacking layers,the overall recognition rate reached 98.7%.Additionally,this database was used for training AI models such as back-propagation networks,convolutional neural networks,residual networks,and long shortterm memory neural networks,with overall recognition rates of 92.8%,95.3%,96.3%,and 96.6%,respectively.This indicates that the TBNN model exhibited better nuclide identification among these AI models,providing an important reference and theoretical basis for the practical application of transformers in the qualitative and quantitative analysis of the γ spectrum.
