课题基金 / 基金详情

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

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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-12-01 至 2024-11-30

项目摘要

项目成果

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中文摘要
翻译
细菌在人体循环系统中的传输在细菌介导的癌症治疗和血液传播细菌感染中具有重要意义。血液中的细菌通过微血管系统或毛细血管壁进入周围组织。细菌在毛细血管中的运动受到自身自身推进、毛细血管大小、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差的影响。细菌还通过各种物理和化学机制附着在毛细血管壁上。所有这些因素都会影响细菌从血液中通过毛细血管壁渗透到周围组织中。细菌在人体内的传输研究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传输和黏附的实验室实验和计算机模拟。这项研究中开发的方法也可以应用于其他病原体,如病毒和真菌,以及药物递送剂。从长远来看,这项拟议的研究有助于发现癌症和传染病的新药物靶点,从而显著影响人类健康。建议的研究内容还将被整合到针对K-12、社区大学、本科生和研究生的跨学科教育和推广经验中,以促进在科学、技术、工程和数学领域中社会经济弱势和种族代表性不足群体的招募和留住。该项目的目标是系统地研究毛细血管结构和流动参数、细菌运动和黏附相互作用以及跨毛细血管压力梯度对正常和渗漏肿瘤微血管中细菌的毛细血管内运输、边缘形成和黏附的作用。这一目标将通过以下具体目标来实现:(1)开发与生理学相关的微流体和毛细管流的计算模型;(2)研究细菌运动以及鞭毛和1型菌毛介导的与内皮的黏附相互作用在细菌边缘形成和黏附内皮中的作用;(3)确定跨毛细管压力梯度在正常和渗漏的肿瘤微血管中细菌运输中的作用。这项拟议的工作具有变革性,因为它将通过建立一套新的流体动力学计算和实验平台,创造关于毛细血管血流动力学、细菌运动性以及细菌-宿主细胞相互作用在细菌运输和传播中的作用的新知识。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
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
EFRI ELiS: Nano-Bio-Hybrid Living Systems for Airborne Biothreat Detection
CAREER: A Biomanufactured Platform for Modulating Immune Cell-Bacteria Interactions in the Tumor Microenvironment
RI: Small: Distributed Network of BacteriaBots
国内基金
海外基金
Computational Methods for Analyzing Toponome Data