Dynamic tracking analysis of monoclonal antibodies(mAbs)biotransformation in vivo is crucial,as certain modifications could inactivate the protein and reduce drug efficacy.However,a particular challenge(i.e.immune rec...Dynamic tracking analysis of monoclonal antibodies(mAbs)biotransformation in vivo is crucial,as certain modifications could inactivate the protein and reduce drug efficacy.However,a particular challenge(i.e.immune recognition deficiencies)in biotransformation studies may arise when modifications occur at the paratope recognized by the antigen.To address this limitation,a multi-epitope affinity technology utilizing the metal organic framework(MOF)@Au@peptide@aptamer composite material was proposed and developed by simultaneously immobilizing complementarity determining region(CDR)mimotope peptide(HH24)and non-CDR mimotope aptamer(CH1S-6T)onto the surface of MOF@Au nanocomposite.Comparative studies demonstrated that MOF@Au@peptide@aptamer exhibited significantly enhanced enrichment capabilities for trastuzumab variants in comparison to mono-epitope affinity technology.Moreover,the higher deamidation ratio for LC-Asn-30 and isomerization ratio for HCAsn-55 can only be monitored by the novel bioanalytical platform based on MOF@Au@peptide@aptamer and liquid chromatography-quadrupole time of flight-mass spectrometry(LC-QTOF-MS).Therefore,multi-epitope affinity technology could effectively overcome the biases of traditional affinity materials for key sites modification analysis of mAb.Particularly,the novel bioanalytical platform can be successfully used for the tracking analysis of trastuzumab modifications in different biological fluids.Compared to the spiked phosphate buffer(PB)model,faster modification trends were monitored in the spiked serum and patients'sera due to the catalytic effect of plasma proteins and relevant proteases.Differences in peptide modification levels of trastuzumab in patients'sera were also monitored.In summary,the novel bioanalytical platform based on the multi-epitope affinity technology holds great potentials for in vivo biotransformation analysis of mAb,contributing to improved understanding and paving the way for future research and clinical applications.展开更多
Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements includ...Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.展开更多
As an interdisciplinary research approach,traditional cognitive science adopts mainly the experiment,induction,modeling,and validation paradigm.Such models are sometimes not applicable in cyber-physical-socialsystems ...As an interdisciplinary research approach,traditional cognitive science adopts mainly the experiment,induction,modeling,and validation paradigm.Such models are sometimes not applicable in cyber-physical-socialsystems (CPSSs),where the large number of human users involves severe heterogeneity and dynamics.To reduce the decision-making conflicts between people and machines in human-centered systems,we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages:descriptive cognition based on artificial cognitive systems (ACSs),predictive cognition with computational deliberation experiments,and prescriptive cognition via parallel behavioral prescription.To make iteration of these stages constantly on-line,a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive knowledge.Preliminary experiments on two representative scenarios,urban travel behavioral prescription and cognitive visual reasoning,indicate that our parallel cognition learning is effective and feasible for human behavioral prescription,and can thus facilitate human-machine cooperation in both complex engineering and social systems.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.:82373829,82273893,and 82173773)the Natural Science Foundation of Guangdong Province,China(Grant Nos.:2021A1515220099,and 2022A1515011576)+1 种基金the High-End Foreign Experts Project,China(Grant No.:G2021199005L)the Science and Technology Program of Guangdong Provincial Medical Products Administration,China(Grant Nos.:2023TDZ11,and 2022ZDB04).
文摘Dynamic tracking analysis of monoclonal antibodies(mAbs)biotransformation in vivo is crucial,as certain modifications could inactivate the protein and reduce drug efficacy.However,a particular challenge(i.e.immune recognition deficiencies)in biotransformation studies may arise when modifications occur at the paratope recognized by the antigen.To address this limitation,a multi-epitope affinity technology utilizing the metal organic framework(MOF)@Au@peptide@aptamer composite material was proposed and developed by simultaneously immobilizing complementarity determining region(CDR)mimotope peptide(HH24)and non-CDR mimotope aptamer(CH1S-6T)onto the surface of MOF@Au nanocomposite.Comparative studies demonstrated that MOF@Au@peptide@aptamer exhibited significantly enhanced enrichment capabilities for trastuzumab variants in comparison to mono-epitope affinity technology.Moreover,the higher deamidation ratio for LC-Asn-30 and isomerization ratio for HCAsn-55 can only be monitored by the novel bioanalytical platform based on MOF@Au@peptide@aptamer and liquid chromatography-quadrupole time of flight-mass spectrometry(LC-QTOF-MS).Therefore,multi-epitope affinity technology could effectively overcome the biases of traditional affinity materials for key sites modification analysis of mAb.Particularly,the novel bioanalytical platform can be successfully used for the tracking analysis of trastuzumab modifications in different biological fluids.Compared to the spiked phosphate buffer(PB)model,faster modification trends were monitored in the spiked serum and patients'sera due to the catalytic effect of plasma proteins and relevant proteases.Differences in peptide modification levels of trastuzumab in patients'sera were also monitored.In summary,the novel bioanalytical platform based on the multi-epitope affinity technology holds great potentials for in vivo biotransformation analysis of mAb,contributing to improved understanding and paving the way for future research and clinical applications.
文摘Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.
基金Project supported by the National Natural Science Foundation of China (Nos.62076237,62073321,and U1811463)the Youth Innovation Promotion Association,Chinese Academy of Sciences (No.2021130)。
文摘As an interdisciplinary research approach,traditional cognitive science adopts mainly the experiment,induction,modeling,and validation paradigm.Such models are sometimes not applicable in cyber-physical-socialsystems (CPSSs),where the large number of human users involves severe heterogeneity and dynamics.To reduce the decision-making conflicts between people and machines in human-centered systems,we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages:descriptive cognition based on artificial cognitive systems (ACSs),predictive cognition with computational deliberation experiments,and prescriptive cognition via parallel behavioral prescription.To make iteration of these stages constantly on-line,a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive knowledge.Preliminary experiments on two representative scenarios,urban travel behavioral prescription and cognitive visual reasoning,indicate that our parallel cognition learning is effective and feasible for human behavioral prescription,and can thus facilitate human-machine cooperation in both complex engineering and social systems.
