Accurate detection of multiple small end-metabolic biomarkers is more sensitive than large biomoleculesto provide real-time feedbacks of physiological/pathologicalstate, but is more challenging due to lack of specific...Accurate detection of multiple small end-metabolic biomarkers is more sensitive than large biomoleculesto provide real-time feedbacks of physiological/pathologicalstate, but is more challenging due to lack of specific identifyinggroups. Current optical platforms suffer from unsatisfactoryresolutions to differentiate each target because they producesimilar output to different targets using a single excitation,and inevitably involve non-functional components that increase chances of interacting with non-target molecules.Herein, by taking full advantage of each building unit’sfunctionality to integrate multivariate recognition elements inone interface, a dual-excitation-driven full-component-responsive metal-organic framework (MOF)-based luminescentprobe, namely CeTMA-TMA-Eu, is successfully custom-tailored for detecting both pseudouridine (Ψ) and N-acetylaspartate (NAA), the diagnostic hallmarks of cancer andneurodegenerative disorder. Remarkably, Ψ interacts withMOF’s organic building unit (trimesic acid, TMA) and filtersout its absorptions of 262 nm-light to reduce its energytransferred to Eu^(3+), while NAA induces the valence transitionof Ce^(4+)/Ce^(3+) nodes to improve the cooperative energy transferefficacy from TMA and Ce^(3+) to Eu^(3+). As a result, this platformexhibits completely reverse photoresponses towards Ψ(“switch-off” at 262 nm excitation) and NAA (“switch-on”upon 296 nm excitation), and demonstrates excellent selectivity and sensitivity in complex biofluids, with low detection limits of 0.16 and 0.15 μM, and wide linear ranges of0–180 and 0–100 μM, respectively. Such full-componentresponsive probe with dual-excitation-mediated reverse responses for multi-small targets intrinsically minimizes its interaction with non-target molecules and amplifies resolutionto discriminate each target, providing a new strategy for improving assay accuracy of multi-small biomarkers in diagnostics.展开更多
Background:A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention.In the...Background:A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention.In the United States,sexually transmitted infections(STIs)are a huge economic burden,with a large fraction of the burden attributed to HIV.Data also show interactions between HIV and other sexually transmitted infections(STIs),such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without.However,given the wide range in prevalence and incidence burdens of STIs,current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling.Further,causal factors for higher risk of coinfection could be both behavioral(i.e.,compounding effects of individual behaviors,network structures,and care behaviors)and biological(i.e.,presence of one disease can biologically increase the risk of another).However,the data on the fraction attributed to each are limited.Methods:We present a new mixed agent-based compartmental(MAC)framework for jointly modeling STIs.It uses a combination of a new agent-based evolving network modeling(ABENM)technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases.As a demonstration,we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2,using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs.We simulated sexual transmissions among heterosexual males,heterosexual females,and men who have sex with men(men only and men and women).Setting the biological risk of co-infection to zero,we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics.Results:The contribution of behavioral factors to risk of coinfection was sensitive to disease burden,care access,and population heterogeneity and mixing.The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here,suggesting potential role of biological factors,that should be investigated further specific to an STI.Conclusions:The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features.The numerical analysis serves as proof-of-concept for the application to STIs.Interactions between diseases are influenced by behavioral factors,are sensitive to care access and population features,and are likely exacerbated by biological factors.Social and economic conditions are among key drivers of behaviors that increase STI transmission,and thus,structural interventions are a key part of behavioral in-terventions.Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention.The new simulation framework is especially suited to simulate behavior as a function of social determinants,and further,to identify optimal combinations of common structural and disease-specific interventions.展开更多
基金supported by the National Key Research and Development Program of China (2022YFC2403203, 2024YFF0508601)the National Natural Science Foundation of China (52172279)+3 种基金the Basic Research Program of Shanghai (21JC1406003)the Shanghai Rising-Star Program (21QA1402200)the Leading Talents in Shanghai in 2018, the Key Field Research Program (2023AB054)the Higher Education Discipline Innovation Project (B14018)。
文摘Accurate detection of multiple small end-metabolic biomarkers is more sensitive than large biomoleculesto provide real-time feedbacks of physiological/pathologicalstate, but is more challenging due to lack of specific identifyinggroups. Current optical platforms suffer from unsatisfactoryresolutions to differentiate each target because they producesimilar output to different targets using a single excitation,and inevitably involve non-functional components that increase chances of interacting with non-target molecules.Herein, by taking full advantage of each building unit’sfunctionality to integrate multivariate recognition elements inone interface, a dual-excitation-driven full-component-responsive metal-organic framework (MOF)-based luminescentprobe, namely CeTMA-TMA-Eu, is successfully custom-tailored for detecting both pseudouridine (Ψ) and N-acetylaspartate (NAA), the diagnostic hallmarks of cancer andneurodegenerative disorder. Remarkably, Ψ interacts withMOF’s organic building unit (trimesic acid, TMA) and filtersout its absorptions of 262 nm-light to reduce its energytransferred to Eu^(3+), while NAA induces the valence transitionof Ce^(4+)/Ce^(3+) nodes to improve the cooperative energy transferefficacy from TMA and Ce^(3+) to Eu^(3+). As a result, this platformexhibits completely reverse photoresponses towards Ψ(“switch-off” at 262 nm excitation) and NAA (“switch-on”upon 296 nm excitation), and demonstrates excellent selectivity and sensitivity in complex biofluids, with low detection limits of 0.16 and 0.15 μM, and wide linear ranges of0–180 and 0–100 μM, respectively. Such full-componentresponsive probe with dual-excitation-mediated reverse responses for multi-small targets intrinsically minimizes its interaction with non-target molecules and amplifies resolutionto discriminate each target, providing a new strategy for improving assay accuracy of multi-small biomarkers in diagnostics.
基金supported by the National Science Foundation,United States,under NSF 1915481the National Institute of Allergy and Infectious Diseases of the National Institutes of Health,United States,under Award Number R01AI127236.
文摘Background:A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention.In the United States,sexually transmitted infections(STIs)are a huge economic burden,with a large fraction of the burden attributed to HIV.Data also show interactions between HIV and other sexually transmitted infections(STIs),such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without.However,given the wide range in prevalence and incidence burdens of STIs,current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling.Further,causal factors for higher risk of coinfection could be both behavioral(i.e.,compounding effects of individual behaviors,network structures,and care behaviors)and biological(i.e.,presence of one disease can biologically increase the risk of another).However,the data on the fraction attributed to each are limited.Methods:We present a new mixed agent-based compartmental(MAC)framework for jointly modeling STIs.It uses a combination of a new agent-based evolving network modeling(ABENM)technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases.As a demonstration,we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2,using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs.We simulated sexual transmissions among heterosexual males,heterosexual females,and men who have sex with men(men only and men and women).Setting the biological risk of co-infection to zero,we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics.Results:The contribution of behavioral factors to risk of coinfection was sensitive to disease burden,care access,and population heterogeneity and mixing.The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here,suggesting potential role of biological factors,that should be investigated further specific to an STI.Conclusions:The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features.The numerical analysis serves as proof-of-concept for the application to STIs.Interactions between diseases are influenced by behavioral factors,are sensitive to care access and population features,and are likely exacerbated by biological factors.Social and economic conditions are among key drivers of behaviors that increase STI transmission,and thus,structural interventions are a key part of behavioral in-terventions.Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention.The new simulation framework is especially suited to simulate behavior as a function of social determinants,and further,to identify optimal combinations of common structural and disease-specific interventions.
