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In vivo Models for Candida Albicans Biofilms Study

Wenrui Gu, Dongmei Xu, Dongmei Guo, Liuping Zhang and Shujuan Sun

Biofilm is a common mode of fungal growth in clinical infection. In the mode of biofilm, Candida albicans tends to display high resistance to body immunity and antimicrobial agents, which has a significant impact on mortality. Biofilm models are essential tools to better understand the mechanisms of formation and resistance. Compared to in vitro models, in vivo models can better take into account the host immune system and are indispensable for the study of medical device related infection. The aim of this review is to summarize information related to the reported in vivo models of C. albicans biofilms, analyze the operating process and application of them, and compare their advantages and limitations. A literature search was performed from databases in Medline (PubMed), Web of Science, Science Direct, and Google scholar by applying some related search terms. The articles related to agriculture, ecology, and synthetic work and those using languages other than English have been excluded. The bibliographies of papers relating to the review subject were also searched for further relevant references. According to the common sites of C. albicans infection; three kinds of in vivo models are discussed in this review: oral mucosa model, vaginal mucosa model and implanted catheter model. The former two models can demonstrate the structure and composition of biofilms growing on the mucosa, and implanted catheter model represents different kinds of medical devices. To expedite the success of new treatments of infection, further refinement of in vivo models is an urgent need.

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Chemical Abstracts Service (CAS)
Open J Gate
Academic Keys
ResearchBible
CiteFactor
Cosmos IF
Open Academic Journals Index (OAJI)
Electronic Journals Library
RefSeek
Hamdard University
IndianScience.in
Scholarsteer
International Innovative Journal Impact Factor (IIJIF)
International Institute of Organised Research (I2OR)
Cosmos
Geneva Foundation for Medical Education and Research
Secret Search Engine Labs

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