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Title: Magnet Patterned Superparamagnetic Fe 3 O 4 /Au Core–Shell Nanoplasmonic Sensing Array for Label‐Free High Throughput Cytokine Immunoassay

Rapid and accurate immune monitoring plays a decisive role in effectively treating immune‐related diseases especially at point‐of‐care, where an immediate decision on treatment is needed upon precise determination of the patient immune status. Derived from the emerging clinical demands, there is an urgent need for a cytokine immunoassay that offers unprecedented sensor performance with high sensitivity, throughput, and multiplexing capability, as well as short turnaround time at low system complexity, manufacturability, and scalability. In this paper, a label‐free, high throughput cytokine immunoassay based on a magnet patterned Fe3O4/Au core–shell nanoparticle (FACSNP) sensing array is developed. By exploiting the unique superparamagnetic and plasmonic properties of the core–shell nanomaterials, a facile microarray patterning technique is established that allows the fabrication of a uniform, self‐assembled microarray on a large surface area with remarkable tunability and scalability. The sensing performance of the FACSNP microarray is validated by real‐time detection of four cytokines in complex biological samples, showing high sensitivity (≈20 pg mL−1), selectivity and throughput with excellent statistical accuracy. The developed immunoassay is successfully applied for rapid determination of the functional immunophenotype of leukemia tumor‐associated macrophages, manifesting its potential clinical applications for real‐time immune monitoring, early cancer detection, and therapeutic drug stratification toward more » personalized medicine.

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Award ID(s):
1701322 1701363
Publication Date:
Journal Name:
Advanced Healthcare Materials
Wiley Blackwell (John Wiley & Sons)
Sponsoring Org:
National Science Foundation
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Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. [2] A. C. Bridi, T. Q. 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