Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn.
Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn.
复制标题
可编程生物 - 纳米芯平台：具有学习能力的即时生物传感器系统。
DOI：
10.1021/acs.accounts.6b00112
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发表时间：
2016-07-19
影响因子：
18.3
通讯作者：
McDevitt JT
中科院分区：
文献类型：
--
作者：
McRae MP;Simmons G;Wong J;McDevitt JT
即时医疗微设备（POC）和机器学习的结合有可能改变医学实践。在这一领域，可扩展的芯片实验室（LOC）设备与标准实验室方法相比具有许多优势，包括更快的分析、更低的成本、更低的功耗以及更高水平的集成和自动化。尽管近年来LOC技术取得了重大进展，但仍有一些障碍阻碍着这些新型医疗微设备的临床应用和市场渗透。同样，虽然机器学习近年来出现了爆炸式增长，并有望将医学实践转向数据密集型和基于证据的决策，但由于临床测量和疾病确定之间缺乏整合，它的应用受到了阻碍。在这篇文章中，我们描述了可编程生物纳米芯片（p-BNC）系统的最新发展，这是一种具有学习能力的生物传感器平台。p-BNC是一个“数字化生物学平台”，其中少量患者样品在琼脂糖珠传感器上产生免疫荧光信号，该信号被光学提取并转化为抗原浓度。该平台包括一次性微流体盒、便携式分析仪、自动数据分析软件和直观的移动健康界面。一次性使用的墨盒是完全集成的，独立的微流体装置，含有水缓冲液，方便嵌入POC使用。开发了一种新的流体输送方法，通过驱动药筒的吸塑包来提供精确且可重复的流量。一种便携式分析仪仪器被设计为集成流体输送、光学检测、图像分析和用户界面，代表了一个通用的系统，用于获取、处理和管理临床数据，同时克服了临床广泛采用LOC技术所面临的许多挑战。我们展示了p-BNC的灵活性，通过在一次性使用的一次性药盒内完成三项临床应用的多重检测：前列腺癌，卵巢癌和急性心肌梗死。为了实现创造“能够学习的传感器”的目标，我们开发并描述了心脏记分卡，这是一种用于心血管疾病谱的临床决策支持系统。Cardiac ScoreCard方法包括一个全面的生物标志物面板和风险因素信息，能够评估心脏病发作和心力衰竭患者的早期风险和晚期疾病进展的预测模型。这些标志物驱动的检测有可能从根本上降低成本，减少等待时间，并为需要定期健康监测的患者提供新的选择。此外，这些努力证明了融合信息丰富的生物标志物和物联网（IoT）数据的临床应用，使用预测分析来生成健康/疾病状态的单指标评估。通过促进疾病预防和个性化健康管理，这种性质的工具有可能以指数方式改善医疗保健。
The combination of point-of-care (POC) medical microdevices and machine learning has the potential transform the practice of medicine. In this area, scalable lab-on-a-chip (LOC) devices have many advantages over standard laboratory methods, including faster analysis, reduced cost, lower power consumption, and higher levels of integration and automation. Despite significant advances in LOC technologies over the years, several remaining obstacles are preventing clinical implementation and market penetration of these novel medical microdevices. Similarly, while machine learning has seen explosive growth in recent years and promises to shift the practice of medicine toward data-intensive and evidence-based decision making, its uptake has been hindered due to the lack of integration between clinical measurements and disease determinations. In this Account, we describe recent developments in the programmable bio-nanochip (p-BNC) system, a biosensor platform with the capacity for learning. The p-BNC is a “platform to digitize biology” in which small quantities of patient sample generate immunofluorescent signal on agarose bead sensors that is optically extracted and converted to antigen concentrations. The platform comprises disposable microfluidic cartridges, a portable analyzer, automated data analysis software, and intuitive mobile health interfaces. The single-use cartridges are fully integrated, self-contained microfluidic devices containing aqueous buffers conveniently embedded for POC use. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the cartridge’s blister packs. A portable analyzer instrument was designed to integrate fluid delivery, optical detection, image analysis, and user interface, representing a universal system for acquiring, processing, and managing clinical data while overcoming many of the challenges facing the widespread clinical adoption of LOC technologies. We demonstrate the p-BNC’s flexibility through the completion of multiplex assays within the single-use disposable cartridges for three clinical applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Toward the goal of creating “sensors that learn”, we have developed and describe here the Cardiac ScoreCard, a clinical decision support system for a spectrum of cardiovascular disease. The Cardiac ScoreCard approach comprises a comprehensive biomarker panel and risk factor information in a predictive model capable of assessing early risk and late-stage disease progression for heart attack and heart failure patients. These marker-driven tests have the potential to radically reduce costs, decrease wait times, and introduce new options for patients needing regular health monitoring. Further, these efforts demonstrate the clinical utility of fusing data from information-rich biomarkers and the Internet of Things (IoT) using predictive analytics to generate single-index assessments for wellness/illness status. By promoting disease prevention and personalized wellness management, tools of this nature have the potential to improve health care exponentially.
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影响因子：
13.3
作者：
Jokerst, Jesse V.;Chou, Jie;Camp, James P.;Wong, Jorge;Lennart, Alexis;Pollard, Amanda A.;Floriano, Pierre N.;Christodoulides, Nicolaos;Simmons, Glennon W.;Zhou, Yanjie;Ali, Mehnaaz F.;McDevitt, John T.
通讯作者：
McDevitt, John T.
影响因子：
37.8
作者：
Sabatine, MS;Morrow, DA;Braunwald, E
通讯作者：
Braunwald, E
影响因子：
105.7
作者：
Kawamoto, K;Houlihan, CA;Lobach, DF
通讯作者：
Lobach, DF
影响因子：
2.8
作者：
Qiu, Xianbo;Thompson, Jason A.;Bau, Haim H.
通讯作者：
Bau, Haim H.
影响因子：
7.5
作者：
Patel VL;Shortliffe EH;Stefanelli M;Szolovits P;Berthold MR;Bellazzi R;Abu-Hanna A
通讯作者：
Abu-Hanna A