Medical Device Enabled by Portable Fluorescence Microscopy and Microfluidics for Monitoring Surgical Inflammation Biomarkers

监测患者对感染的个体反应不仅对早期诊断至关重要，而且对确定患者的有效治疗也是至关重要的。在美国，大约有1400万例手术是在住院期间进行的，有超过10万人随后患上了手术部位感染(SSI)。它的管理和治疗每年又花费100亿美元的超额成本。此外，77%的外科病人的死亡归因于SSI。这项授予新不伦瑞克罗格斯大学的奖项将开发一种生物医学平台，以快速而准确地进行指定的诊断测试。与目前使用的用于这些测试的仪器相比，该奖项将实现显著的进步。这些测试使用的仪器体积大、成本高，需要经过技术培训的工作人员手动处理样品才能执行这些测试。未来，建议的平台可以翻译到多个医疗保健环境中，包括初级保健办公室、紧急护理环境、重症监护病房和检测实验室。由该奖项产生的生物医学平台将具有造福人类健康和福利的巨大潜力。该项目将培养生物传感器、微流体、微制造、光学和机器学习领域的本科生和研究生。该项目还将使拟议的研究能够纳入调查人员的教育工作。此外，该奖项将促进调查人员开展各种外展活动，包括通过教育讲座吸引K-12学生参与研究，并通过在线提供这些课程以广泛传播知识。患者对感染的个性化免疫反应监测不仅对于早期诊断至关重要，而且对于确定有效的治疗方法也至关重要，从而对患者的预后产生重大影响。SSI是手术后患者面临的一种常见情况。SSI的早期视觉症状包括伤口部位的炎症和猫咪、疼痛、发烧和不适。紧随其后的是微生物培养，需要几天的时间才能得到结果，这在治疗途径上留下了巨大的诊断缺口。高危患者术后频繁检测临床认可的生物标记物(如C反应蛋白(CRP)、降钙素原(PCT))可提供SSI的早期指征，然而，他们的检测需要集中的实验室设施、训练有素的专业人员和更长的等待时间才能得到结果。在这项工作中，研究人员设想了一个仅使用一滴全血来定量炎症蛋白的生物医学平台。这项拟议的创新基于将光子学、微流体、智能手机光学传感和3-D多层微流体架构交叉集成到单个生物芯片上，并具有自动化全血处理功能，以提供患者样本的临床测试结果。传感器将配备实时测量能力和机器学习模型，以训练传感器数据并提供测试结果。传感器将以从罗伯特·伍德·约翰逊医疗医院收集的患者样本为基准。研究人员设想，拟议的生物传感器平台将是通用的，预先功能化的墨盒可以更换为不同的传染病生物标记物测试。这项变革性的研究还将开启新的教育倡议，以培养下一代工程师和科学家。这一奖项反映了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。

Monitoring patients’ individual response to infections is vital not only for early diagnosis but also to determine effective treatment for the patients. In United States, around 14 million operative procedures are performed in inpatient hospital settings with more than hundred thousand people subsequently developing surgical site infections (SSI). Its management and treatment cost another US $10 billion yearly in excess costs. Further, 77% of the deaths of surgical patients are attributed to SSI. This award which is being made to Rutgers University New Brunswick will develop a biomedical platform to perform prescribed diagnostics tests rapidly and accurately. The award will enable a significant advance compared to currently employed instruments used for these tests which are bulky, costly, and requires manual sample processing by technically trained staff to perform these tests. In future, proposed platform can translated in multiple healthcare settings including primary care offices, emergency care settings, intensive care units and testing laboratories. Biomedical platform resulting from this award will have a great potential to benefit human health and welfare. This project will train undergraduate and graduate students in the fields of biosensors, microfluidics, microfabrication, optics, and machine learning. The project will also enable the integration of proposed research into investigators educational efforts. Further, the award will facilitate the investigators to implement various outreach activities including engaging K-12 students in research, and the public through educational lectures and making them available online for broad dissemination of knowledge.Personalized immune response monitoring of patients to infections is critical not only for early diagnosis but also for determining effective therapy, thereby having a significant impact in patients’ outcome. SSI is a common condition faced by patients’ post-surgical procedures. Early visual indications of SSI include inflammation and puss at the site of the wound, pain, fever, and discomfort. This is followed by a microbial culture which takes multiple days to get the results, leaving huge diagnostic gaps in the treatment pathway. Post-operative frequent quantification of clinically approved biomarkers (e.g., C-reactive protein (CRP), procalcitonin (PCT)) on high-risk patients could provide an early indication of an SSI, however, their testing requires centralized lab facility, trained professionals and longer wait times to get the results. In this work, investigators envision a biomedical platform for inflammatory proteins quantification using only a drop of whole blood. The proposed innovation is based on a cross-cut integration of photonics, microfluidics, smartphone enabled optical sensing, and 3-D multi-layer microfluidic architectures on a single biochip with automated whole blood processing to provide clinical test results from patient samples. Sensors will be equipped with real-time measurement capability and machine learning models to train the sensors data and provide test results. Sensors will be benchmarked with patient samples collected from Robert Wood Johnson Medical Hospital. Investigators envisions proposed biosensor platform to be generic and the pre-functionalized cartridges can be swapped out for different infectious diseases biomarkers testing. This transformative research will also open new educational initiatives to train the next generation of engineers and scientists.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.