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   https://doi.org/10.1039/c7lc01183k  

   Cui, Xin ; Liu, Ya ; Hu, Dinglong ; Qian, Weiyi ; Tin, Chung ; Sun, Dong ; Chen, Weiqiang ; Lam, Raymond H. ( January 2018 , Lab on a Chip)

   Quantitative and dynamic analyses of immune cell secretory cytokines are essential for precise determination and characterization of the “immune phenotype” of patients for clinical diagnosis and treatment of immune-related diseases. Although multiple methods including the enzyme-linked immunosorbent assay (ELISA) have been applied for cytokine detection, such measurements remain very challenging in real-time, high-throughput, and high-sensitivity immune cell analysis. In this paper, we report a highly integrated microfluidic device that allows for on-chip isolation, culture, and stimulation, as well as sensitive and dynamic cytokine profiling of immune cells. Such a microfluidic sensing chip is integrated with cytometric fluorescent microbeads for real-time and multiplexed monitoring of immune cell cytokine secretion dynamics, consuming a relatively small extracted sample volume (160 nl) without interrupting the immune cell culture. Furthermore, it is integrated with a Taylor dispersion-based mixing unit in each detection chamber that shortens the immunoassay period down to less than 30 minutes. We demonstrate the profiling of multiple pro-inflammatory cytokine secretions ( e.g. interleukin-6, interleukin-8, and tumor necrosis factors) of human peripheral blood mononuclear cells (PBMCs) with a sensitivity of 20 pg ml −1 and a sample volume of 160 nl per detection. Further applications of this automated, rapid, and high-throughput microfluidic immunophenotypingmore »platform can help unleash the mechanisms of systemic immune responses, and enable efficient assessments of the pathologic immune status for clinical diagnosis and immune therapy.« less
2. Construction of Magnetic Core–Large Mesoporous Satellite Immunosensor for Long‐Lasting Chemiluminescence and Highly Sensitive Tumor Marker Determination

   https://doi.org/10.1002/smll.202304631  

   Liu, Shude ; Li, Juan ; Zou, Yidong ; Jiang, Yongjian ; Wu, Limin ; Deng, Yonghui ( July 2023 , Small)

   Chemiluminescence immunoassay exhibits high sensitivity and signal‐to‐noise ratio, thus attracting great attention in the early diagnosis and dynamic monitoring of diseases. However, the collection of conventional flash‐type chemiluminescence signal (<5 s) relies heavily on automatic sampling and reading instrument. Herein, a novel core‐satellite multifunctional chemiluminescence immunosensor is designed for the efficient enrichment and highly sensitive determination of cancer biomarker carcinoembryonic antigen (CEA) with enhanced and long‐lasting output signal that can be conveniently recorded by a simple microplate plate reading instrument. Anti‐CEA monoclonal antibody 2 (Ab2) modified Fe₃O₄@SiO₂microspheres (Fe₃O₄@SiO₂‐Ab2, 370 nm in diameter) are synthesized as the core for selectively capturing and enriching target CEA in solution, and anti‐human CEA monoclonal antibody 1 (Ab1) and horseradish peroxidase (HRP) co‐immobilized dendritic large‐mesoporous silica nanospheres (MSNs‐HRP/Ab1, 80 nm in diameter, pore size: 17 nm) are synthesized as the satellite for efficient immunological recognition and signal amplification. The as‐designed core‐satellite magnetic chemiluminescence immunosensors exhibit a broad linear range of 0.01−20 ng mL⁻¹and a low detection limit of 3.0 pg mL⁻¹for the convenient, highly specific, and sensitive determination of CEA in human serum. Such core‐satellite chemiluminescence immunosensors are expected to act as a powerful tool for in vitro detection of various biomarkers, overcome the defect of conventional chemiluminescence relying heavily on expensivemore »and bulky automatic instruments and popularize chemiluminescence analysis to primary medical institutions and remote areas.

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3. Rapid single-molecule digital detection of protein biomarkers for continuous monitoring of systemic immune disorders

   https://doi.org/10.1182/blood.2019004399  

   Song, Yujing ; Sandford, Erin ; Tian, Yuzi ; Yin, Qingtian ; Kozminski, Andrew G. ; Su, Shiuan-Haur ; Cai, Tao ; Ye, Yuxuan ; Chung, Meng Ting ; Lindstrom, Ryan ; et al ( March 2021 , Blood)

   Abstract Digital protein assays have great potential to advance immunodiagnostics because of their single-molecule sensitivity, high precision, and robust measurements. However, translating digital protein assays to acute clinical care has been challenging because it requires deployment of these assays with a rapid turnaround. Herein, we present a technology platform for ultrafast digital protein biomarker detection by using single-molecule counting of immune-complex formation events at an early, pre-equilibrium state. This method, which we term “pre-equilibrium digital enzyme-linked immunosorbent assay” (PEdELISA), can quantify a multiplexed panel of protein biomarkers in 10 µL of serum within an unprecedented assay incubation time of 15 to 300 seconds over a 104 dynamic range. PEdELISA allowed us to perform rapid monitoring of protein biomarkers in patients manifesting post-chimeric antigen receptor T-cell therapy cytokine release syndrome, with ∼30-minute sample-to-answer time and a sub–picograms per mL limit of detection. The rapid, sensitive, and low-input volume biomarker quantification enabled by PEdELISA is broadly applicable to timely monitoring of acute disease, potentially enabling more personalized treatment.
4. Phase‐Regulated Sensing Mechanism of MoS ₂ Based Nanohybrids toward Point‐of‐Care Prostate Cancer Diagnosis

   https://doi.org/10.1002/smll.202000307  

   Ying, Zi ; Feng, Lingyan ; Ji, Dongqing ; Zhang, Yuan ; Chen, Wei ; Dai, Yifan ; Janyasupab, Metini ; Li, Xinxin ; Wen, Weijia ; Liu, Chung‐Chiun ( April 2020 , Small)

   Alpha‐methylacyl‐CoA racemase (AMACR) has been proven to be consistently overexpressed in prostate cancer epitheliums, and is expected to act as a positive biomarker for the diagnosis of prostate carcinoma in clinical practice. Here, a strategy for specific determination of AMACR in real human serum by using an electrochemical microsensor system is presented. In order to implement the protocol, a self‐organized nanohybrid consisting of metal nanopillars in a 2D MoS₂matrix is developed as material for the sensing interface. The testing signal outputs are strongly enhanced with the presence of the nanohybrids owing to that the metal pillars provide an efficient mass difussion and electron transfer path to the MoS₂film surface. Furthermore, the phase‐regulated sensing mechanism over MoS₂is noticed and demonstrated by density functional theory calculation and experiments. The explored MoS₂based nanohybrids are employed for the fabrication of an electrochemical microsensor, presenting good linear relationship in both ng µL⁻¹and pg µL⁻¹ranges for AMACR quantification. The sampling analysis of human serum indicates that this microsensor has good diagnostic specificity and sensitivity toward AMACR. The proposed electrochemical microsensor system also demonstrates the advantages of convenience, cost‐effectiveness, and disposability, resulting in a potential integrated microsystem for point‐of‐care prostate cancer diagnosis.
5. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEGmore »channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. 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: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. 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. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less