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1. One-pot synthesis of enzyme@metal–organic material (MOM) biocomposites for enzyme biocatalysis

   https://doi.org/DOI https://doi.org/10.1039/D1GC00775K  

   Pan, Y. ; Li, H. ; Lenertz, M. ; Han, Y. ; Ugrinov, A. ; Kilin, D. ; Chen, B. ; Yang, Z. ( March 2021 , Green chemistry)

   Metal–organic frameworks/materials (MOFs/MOMs) are advanced enzyme immobilization platforms that improve biocatalysis, materials science, and protein biophysics. A unique way to immobilize enzymes is co-crystallization/co-precipitation, which removes the limitation on enzyme/substrate size. Thus far, most enzyme@MOF composites rely on the use of non-sustainable chemicals and, in certain cases, heavy metals, which not only creates concerns regarding environmental conservation but also limits their applications in nutrition and biomedicine. Here, we show that a dimeric compound derived from lignin, 5,5′-dehydrodivanillate (DDVA), co-precipitates with enzymes and low-toxicity metals, Ca2+ and Zn2+, and forms stable enzyme@Ca/Zn–MOM composites. We demonstrated this strategy on four enzymes withmore »different isoelectric points (IEPs), molecular weights, and substrate sizes. Furthermore, we found that all enzymes displayed slightly different but reasonable catalytic efficiencies upon immobilization in the Ca–DDVA and Zn–DDVA MOMs, as well as reasonable reusability in both composites. We then probed the structural basis of such differences using a representative enzyme and found enhanced restriction of enzymes in Zn–DDVA than in Ca–DDVA, which might have caused the activity difference. To the best of our knowledge, this is the first aqueous-phase, one-pot synthesis of a lignin-derived “green” enzyme@MOF/MOM platform that can host enzymes without any limitations on enzyme IEP, molecular weight, and substrate size. The different morphologies and crystallinities of the composites formed by Ca–DDVA and Zn–DDVA MOMs broaden their applications depending on the problem of interest. Our approach of enzyme immobilization not only improves the sustainability/reusability of almost all enzymes but also reduces/eliminates the use of non-sustainable resources. This synthesis method has a negligible environmental impact while the products are non-toxic to living things and the environment. The biocompatibility also makes it possible to carry out enzyme delivery/release for nutritional or biomedical applications via our “green” biocomposites.« less
2. Reassessing enzyme kinetics: Considering protease-as-substrate interactions in proteolytic networks

   https://doi.org/10.1073/pnas.1912207117  

   Ferrall-Fairbanks, Meghan C. ; Kieslich, Chris A. ; Platt, Manu O. ( January 2020 , Proceedings of the National Academy of Sciences)

   Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working inmore »one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

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3. A general Ca-MOM platform with enhanced acid-base stability for enzyme biocatalysis

   https://doi.org/https://doi.org/10.1016/j.checat.2021.03.001  

   Pan, Y. ; Li, Q. ; Li, H. ; Farmakes, J. ; Ugrinov, A. ; Zhu, X. ; Lai, Z. ; Chen, B. ; Yang, Z. ( April 2021 , Chem catalysis)

   Co-precipitation of enzymes in metal-organic frameworks is a unique enzyme-immobilization strategy but is challenged by weak acid-base stability. To overcome this drawback, we discovered that Ca2+ can co-precipitate with carboxylate ligands and enzymes under ambient aqueous conditions and form enzyme@metal-organic material composites stable under a wide range of pHs (3.7–9.5). We proved this strategy on four enzymes with varied isoelectric points, molecular weights, and substrate sizes—lysozyme, lipase, glucose oxidase (GOx), and horseradish peroxidase (HRP)—as well as the cluster of HRP and GOx. Interestingly, the catalytic efficiency of the studied enzymes was found to depend on the ligand, probing the originsmore »of which resulted in a correlation among enzyme backbone dynamics, ligand selection, and catalytic efficiency. Our approach resolved the long-lasting stability issue of aqueous-phase co-precipitation and can be generalized to biocatalysis with other enzymes to benefit both research and industry.« less
4. 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 describemore »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 EEG 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
5. Detection of early-stage lung cancer with an in vitro panel of activity-based biosensors to measure inflammatory protease enzymes.

   https://doi.org/10.1200/JCO.2022.40.16_suppl.e20551  

   Dempsey, Paul W. ; Aparicio, Cristina-Mihaela Sandu ; Hantula, Spencer ; Covarrubias, Jose ; Varghese, Sunita ; Neri, Raul ; Ehsan, Sumia ; Covarrubias-Zambrano, Obdulia ; Bossmann, Stefan H. ( June 2022 , Journal of Clinical Oncology)

   e20551 Background: Enzyme activity is at the center of all biological processes. When these activities are misregulated by changes in sequence, expression, or activity, pathologies emerge. Misregulation of protease enzymes such as Matrix Metalloproteinases and Cathepsins play a key role in the pathophysiology of cancer. We describe here a novel class of graphene-based, cost effective biosensors that can detect altered protease activation in a blood sample from early stage lung cancer patients. Methods: The Gene Expression Omnibus (GEO) tool was used to identify proteases differentially expressed in lung cancer and matched normal tissue. Biosensors were assembled on a graphene backbonemore »annotated with one of a panel of fluorescently tagged peptides. The graphene quenches fluorescence until the peptide is either cleaved by active proteases or altered by post-translational modification. 19 protease biosensors were evaluated on 431 commercially collected serum samples from non-lung cancer controls (69%) and pathologically confirmed lung cancer cases (31%) tested over two independent cohorts. Serum was incubated with each of the 19 biosensors and enzyme activity was measured indirectly as a continuous variable by a fluorescence plate reader. Analysis was performed using Emerge, a proprietary predictive and classification modeling system based on massively parallel evolving “Turing machine” algorithms. Each analysis stratified allocation into training and testing sets, and reserved an out-of-sample validation set for reporting. Results: 256 clinical samples were initially evaluated including 35% cancer cases evenly distributed across stages I (29%), II (26%), III (24%) and IV (21%). The case controls included common co-morbidies in the at-risk population such as COPD, chronic bronchitis, and benign nodules (19%). Using the Emerge classification analysis, biosensor biomarkers alone (no clinical factors) demonstrated Sensitivity (Se.) = 92% (CI 82%-99%) and Specificity (Sp.) = 82% (CI 69%-91%) in the out-of-sample set. An independent cohort of 175 clinical cases (age 67±8, 52% male) focused on early detection (26% cancer, 70% Stage I, 30% Stage II/III) were similarly evaluated. Classification showed Se. = 100% (CI 79%-100%) and Sp. = 93% (CI 80%-99%) in the out-of-sample set. For the entire dataset of 175 samples, Se. = 100% (CI 92%-100%) and Sp. = 97% (CI 92%-99%) was observed. Conclusions: Lung cancer can be treated if it is diagnosed when still localized. Despite clear data showing screening for lung cancer by Low Dose Computed Tomography (LDCT) is effective, screening compliance remains very low. Protease biosensors provide a cost effective additional specialized tool with high sensitivity and specificity in detection of early stage lung cancer. A large prospective trial of at-risk smokers with follow up is being conducted to evaluate a commercial version of this assay.« less