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Zhongguo Zhong Yao Za Zhi ; 46(12): 3165-3170, 2021 Jun.
Artigo em Chinês | MEDLINE | ID: mdl-34467709


Nucleic acid aptamers, broad-spectrum target-specific single-stranded oligonucleotides, serve as molecules in targeted therapy, targeted delivery and disease diagnosis for the treatment of tumor or microbial infection and clinical detection. Due to the existence of components in the use of traditional Chinese medicine(TCM), the target is difficult to concentrate and the specificity of treatment is poor. The effective components of TCM are toxic components, so a highly sensitive detection method is urgently needed to reduce the toxicity problem at the same time. The combined application of TCM and modern medical treatment strategy are difficult and cannot improve the therapeutic effect. Aptamers, advantageous in biosensors, aptamer-nanoparticles for targeted drug delivery, and aptamer-siRNA chimeras, are expected to connect Chinese medicinals with nanotechnology, diagnostic technology and combined therapies. We summarized the preparation, screening, and modification techniques of nucleic acid aptamers and the biomedical applications and advantages in therapy, targeting, and diagnosis, aiming at providing a reference for the in-depth research and development in TCM.

Aptâmeros de Nucleotídeos , Ácidos Nucleicos , Sistemas de Liberação de Medicamentos , Medicina Tradicional Chinesa , RNA Interferente Pequeno
J Theor Biol ; 477: 36-43, 2019 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-31194985


This paper proposes two spatio-temporal epidemic network models based on popularity and similarity optimization (PSO), called r-SI and r-SIS, respectively, in which new connections take both popularity and similarity into account. In the spatial dimension, the epidemic process is described by the diffusion equation; in the time dimension, the growth of an epidemic is described by the logistic map. Both models are represented by partial differential equations, and can be easily solved. Simulations are performed on both artificial and real networks, demonstrating the effectiveness of the two models.

Simulação por Computador , Epidemias , Modelos Biológicos , Redes Neurais de Computação , Humanos
Physica A ; 416: 208-218, 2014 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-32288090


In this paper, we study epidemic spreading in metapopulation networks wherein each node represents a subpopulation symbolizing a city or an urban area and links connecting nodes correspond to the human traveling routes among cities. Differently from previous studies, we introduce a heterogeneous infection rate to characterize the effect of nodes' local properties, such as population density, individual health habits, and social conditions, on epidemic infectivity. By means of a mean-field approach and Monte Carlo simulations, we explore how the heterogeneity of the infection rate affects the epidemic dynamics, and find that large fluctuations of the infection rate have a profound impact on the epidemic threshold as well as the temporal behavior of the prevalence above the epidemic threshold. This work can refine our understanding of epidemic spreading in metapopulation networks with the effect of nodes' local properties.

Physica A ; 392(19): 4242-4251, 2013 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32288087


In this paper, explicitly considering the influences of an epidemic outbreak on human travel, a time-varying human mobility pattern is introduced to model the time variation of global human travel. The impacts of the pattern on epidemic dynamics in heterogeneous metapopulation networks, wherein each node represents a subpopulation with any number of individuals, are investigated by using a mean-field approach. The results show that the pattern does not alter the epidemic threshold, but can slightly lower the final average density of infected individuals as a whole. More importantly, we also find that the pattern produces different impacts on nodes with different degree, and that there exists a critical degree k c . For nodes with degree smaller than k c , the pattern produces a positive impact on epidemic mitigation; conversely, for nodes with degree larger than k c , the pattern produces a negative impact on epidemic mitigation.