Experimental and Computational Models of Bacteria Transport and Adhesion in the Microvasculature

微血管中细菌运输和粘附的实验和计算模型

• 批准号: 2133739
• 负责人: Bahareh Behkam
• 金额: $ 49.35万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133739&HistoricalAwards=false
• 关键词: Experimental; Computational; Models; Bacteria; Transport

Bacteria transport in the human circulatory system has important implications in bacteria-mediated cancer therapy and blood-borne bacterial infection. Bacteria in the bloodstream enter into the surrounding tissue through microvasculature or capillary walls. The motion of bacteria in capillaries is influenced by their own self-propulsion, capillary size, the presence of red blood cells in the capillary, and the pressure difference between the capillary and the surrounding tissue. Bacteria also adhere to the capillary wall through various physical and chemical mechanisms. All these factors influence the penetration of bacteria from the bloodstream into the surrounding tissue through the capillary wall. Study of bacteria transport in the human body is very challenging; therefore, this research proposes laboratory experiments and computer modeling of bacteria transport and adhesion in capillaries under physiologically relevant conditions. The methods developed in this research can also be applied to other pathogens such as viruses and fungi, as well as drug delivery agents. The proposed research can significantly impact human health by contributing to the discovery of new drug targets in cancer and infectious diseases in the long term. The proposed research elements will also be integrated into interdisciplinary educational and outreach experiences for K-12, community college, undergraduate, and graduate students to enhance recruitment and retention of the socioeconomically disadvantaged and ethnically underrepresented groups in science, technology, engineering, and mathematics.The goal of this project is to systematically investigate the role of the capillary structure and flow parameters, bacterial motility and adhesive interactions, and transcapillary pressure gradient on the intracapillary transport, margination, and adhesion of bacteria in normal and leaky tumor microvasculature. This goal will be achieved through the following specific aims: (1) Development of physiologically relevant microfluidic and computational models of capillary flow; (2) Investigation of the role of bacteria motility and flagella- and type-1 pili-mediated adhesive interactions with the endothelium in bacteria margination and adhesion to the endothelium; (3) Determination of the role of transcapillary pressure gradient on bacteria transport in normal and leaky tumor microvasculature. The proposed work is transformative as it will create new knowledge about the role of capillary hemodynamics, bacterial motility, and bacteria-host cell interactions in the transport and dissemination of bacteria by establishing a new suite of computational and experimental platforms in Fluid Dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人体循环系统中的细菌转运在细菌介导的癌症治疗和血液传播的细菌感染中具有重要意义。血液中的细菌通过微血管或毛细血管壁进入周围组织。细菌在毛细血管中的运动受其自身的自推进、毛细血管的大小、毛细血管中红细胞的存在以及毛细血管与周围组织之间的压力差的影响。细菌也通过各种物理和化学机制粘附在毛细血管壁上。所有这些因素都会影响细菌从血流通过毛细血管壁渗透到周围组织中。研究细菌在人体内的运输是非常具有挑战性的;因此，本研究提出了在生理相关条件下细菌在毛细血管中的运输和粘附的实验室实验和计算机建模。这项研究开发的方法也可以应用于其他病原体，如病毒和真菌，以及药物输送剂。拟议的研究可以通过促进发现癌症和传染病的新药物靶标来显著影响人类健康。建议的研究元素也将被整合到跨学科的教育和推广经验，为K-12，社区学院，本科生和研究生，以提高招聘和保留的社会经济弱势群体和种族代表性不足的群体在科学，技术，工程和数学。细菌运动性和粘附相互作用，以及正常和渗漏肿瘤微血管中细菌在毛细血管内转运、边缘化和粘附上的跨毛细血管压力梯度。这一目标将通过以下具体目标来实现：（1）发展毛细血管流动的生理学相关的微流体和计算模型;（2）研究细菌运动性和鞭毛和1型菌毛介导的与内皮的粘附相互作用在细菌附边和粘附到内皮中的作用;（3）确定跨毛细血管压力梯度对正常和渗漏肿瘤微血管中细菌转运的作用。拟议的工作是变革性的，因为它将创造关于毛细血管血流动力学，细菌运动，和细菌-通过建立一套新的流体动力学计算和实验平台，研究细菌运输和传播中的宿主细胞相互作用。该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Motion Enhanced Multi‐Level Tracker (MEMTrack): A Deep Learning‐Based Approach to Microrobot Tracking in Dense and Low‐Contrast Environments

运动增强型多级跟踪器 (MEMTrack)：一种基于深度学习的方法，用于在密集和低对比度环境中跟踪微型机器人

• DOI: 10.1002/aisy.202300590
• 发表时间: 2024
• 期刊: Advanced Intelligent Systems
• 影响因子: 7.4
• 作者: Sawhney, Medha;Karmarkar, Bhas;Leaman, Eric J.;Daw, Arka;Karpatne, Anuj;Behkam, Bahareh
• 通讯作者: Behkam, Bahareh