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生物传感器在蚕模型中应用于复苏传染病管理方面的最新进展:一种对抗微生物病原体的新方法。

Recent advances in the biosensors application for reviving infectious disease management in silkworm model: a new way to combat microbial pathogens.

机构信息

Chemical Biology Laboratory, Department of Sericulture, Raiganj University, Raiganj, Uttar Dinajpur, West Bengal, 733134, India.

出版信息

Arch Microbiol. 2024 Apr 5;206(5):206. doi: 10.1007/s00203-024-03933-5.

DOI:10.1007/s00203-024-03933-5
PMID:38575737
Abstract

Silkworms are an essential economic insect but are susceptible to diseases during rearing, leading to yearly losses in cocoon production. While chemical control is currently the primary method to reduce disease incidences, its frequent use can result in loss of susceptibility to pathogens and, ultimately, antibiotic resistance. To effectively prevent or control disease, growers must accurately, sensitively, and quickly detect causal pathogens to determine the best management strategies. Accurate recognition of diseased silkworms can prevent pathogen transmission and reduce cocoon loss. Different pathogen detection methods have been developed to achieve this objective, but they need more precision, specificity, consistency, and promptness and are generally unsuitable for in-situ analysis. Therefore, detecting silkworm diseases under rearing conditions is still an unsolved problem. As a consequence of this, there is an enormous interest in the development of biosensing systems for the early and precise identification of pathogens. There is also significant room for improvement in translating novel biosensor techniques to identify silkworm pathogens. This study explores the types of silkworm diseases, their symptoms, and their causal microorganisms. Moreover, we compare the traditional approaches used in silkworm disease diagnostics along with the latest sensing technologies, with a precise emphasis on lateral flow assay-based biosensors that can detect and manage silkworm pathogens.

摘要

家蚕是一种重要的经济昆虫,但在饲养过程中容易患病,导致每年蚕茧产量损失。虽然化学控制目前是减少疾病发生率的主要方法,但频繁使用会导致对病原体的敏感性降低,最终导致抗生素耐药性。为了有效预防或控制疾病,养殖户必须准确、敏感、快速地检测致病病原体,以确定最佳管理策略。准确识别患病的家蚕可以防止病原体传播并减少蚕茧损失。已经开发了不同的病原体检测方法来实现这一目标,但它们需要更高的精度、特异性、一致性和及时性,并且通常不适合现场分析。因此,在饲养条件下检测家蚕疾病仍然是一个未解决的问题。因此,人们对开发用于早期和精确识别病原体的生物传感系统产生了极大的兴趣。将新型生物传感器技术转化为识别家蚕病原体也有很大的改进空间。本研究探讨了家蚕疾病的类型、症状及其致病微生物。此外,我们比较了传统的家蚕疾病诊断方法和最新的传感技术,重点介绍了基于横向流动分析的生物传感器,该传感器可以检测和管理家蚕病原体。

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Recent advances in the biosensors application for reviving infectious disease management in silkworm model: a new way to combat microbial pathogens.生物传感器在蚕模型中应用于复苏传染病管理方面的最新进展:一种对抗微生物病原体的新方法。
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本文引用的文献

1
Diagnosis of Pebrine Disease in Silkworm Using Molecular Methods.利用分子方法诊断家蚕微粒子病。
Arch Razi Inst. 2023 Aug 31;78(4):1185-1191. doi: 10.32592/ARI.2023.78.4.1185. eCollection 2023 Aug.
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A review of nanomaterials for biosensing applications.用于生物传感应用的纳米材料综述。
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Multi-omics study and ncRNA regulation of anti-BmNPV in silkworms, : an update.家蚕抗BmNPV的多组学研究与非编码RNA调控:最新进展
Front Microbiol. 2023 May 18;14:1123448. doi: 10.3389/fmicb.2023.1123448. eCollection 2023.
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Insects and Public Health: An Overview.昆虫与公共卫生:概述
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Arch Razi Inst. 2022 Aug 31;77(4):1473-1480. doi: 10.22092/ARI.2022.356482.1853. eCollection 2022 Aug.
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Automated Prototype for Cocoon Sorting Attempts to Improve Silk Quality and Production Efficiency through Multi-Step Approach and Machine Learning Algorithms.茧丝自动分拣原型通过多步骤方法和机器学习算法尝试提高丝绸质量和生产效率。
Sensors (Basel). 2023 Jan 12;23(2):868. doi: 10.3390/s23020868.
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Potential of nanobiosensor in sustainable agriculture: the state-of-art.纳米生物传感器在可持续农业中的潜力:现状
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8
Lens on Tropical Sericulture Development in Indonesia: Recent Status and Future Directions for Industry and Social Forestry.印度尼西亚热带养蚕业发展透视:产业与社会林业的现状及未来方向
Insects. 2022 Oct 8;13(10):913. doi: 10.3390/insects13100913.
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The gut microbiota of silkworm are altered by antibiotic exposure.抗生素暴露会改变家蚕的肠道微生物群。
FEBS Open Bio. 2022 Dec;12(12):2203-2212. doi: 10.1002/2211-5463.13502. Epub 2022 Nov 14.
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Recent advances in loop-mediated isothermal amplification (LAMP) for rapid and efficient detection of pathogens.用于快速高效检测病原体的环介导等温扩增技术(LAMP)的最新进展。
Curr Res Microb Sci. 2022 Feb 23;3:100120. doi: 10.1016/j.crmicr.2022.100120. eCollection 2022.