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Ho RJ, Yeh K, Liu YT, Bhargava R. Sensitive Discrete Frequency Mid-Infrared Absorption Spectroscopy Using Digitally Referenced Detection. Anal Chem 2024; 96:8990-8998. [PMID: 38771296 DOI: 10.1021/acs.analchem.4c00134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Broadly tunable mid-infrared (IR) lasers, including quantum cascade lasers (QCL), are an asset for vibrational spectroscopy wherein high-intensity, coherent illumination can target specific spectral bands for rapid, direct chemical detection with microscopic localization. These emerging spectrometers are capable of high measurement throughputs with large detector signals from the high-intensity lasers and fast detection speeds as short as a single laser pulse, challenging the decades old benchmarks of Fourier transform infrared spectroscopy. However, noise in QCL emissions limits the feasible acquisition time for high signal-to-noise ratio (SNR) data. Here, we present an implementation that is broadly compatible with many laser-based spectrometer and microscope designs to address these limitations by leveraging high-speed digitizers and dual detectors to digitally reference each pulse individually. Digitally referenced detection (DRD) is shown to improve measurement sensitivity, with broad spectral indifference, regardless of imbalance due to dissimilarities among system designs or component manufacturers. We incorporated DRD into existing instruments and demonstrated its generalizability: a spectrometer with a 10-fold reduction in spectral noise, a microscope with reduced pixel dwell times to as low as 1 pulse while maintaining SNR normally achieved when operating 8-fold slower, and finally, a spectrometer to measure vibrational circular dichroism (VCD) with a ∼ 4-fold reduction in scan times. The approach not only proves versatile and effective but can also be tailored for specific applications with minimal hardware changes, positioning it as a simple and promising module for spectrometer designs using lasers.
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Affiliation(s)
- Ruo-Jing Ho
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Kevin Yeh
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Yen-Ting Liu
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Rohit Bhargava
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemical and Biomolecular Engineering, Department of Chemistry, Department of Mechanical Science and Engineering, and Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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2
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Sharma VJ, Singh A, Grant JL, Raman J. Point-of-care diagnosis of tissue fibrosis: a review of advances in vibrational spectroscopy with machine learning. Pathology 2024; 56:313-321. [PMID: 38341306 DOI: 10.1016/j.pathol.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/24/2023] [Accepted: 11/01/2023] [Indexed: 02/12/2024]
Abstract
Histopathology is the gold standard for diagnosing fibrosis, but its routine use is constrained by the need for additional stains, time, personnel and resources. Vibrational spectroscopy is a novel technique that offers an alternative atraumatic approach, with short scan times, while providing metabolic and morphological data. This review evaluates vibrational spectroscopy for the assessment of fibrosis, with a focus on point-of-care capabilities. OVID Medline, Embase and Cochrane databases were systematically searched using PRISMA guidelines for search terms including vibrational spectroscopy, human tissue and fibrosis. Studies were stratified based on imaging modality and tissue type. Outcomes recorded included tissue type, machine learning technique, metrics for accuracy and author conclusions. Systematic review yielded 420 articles, of which 14 were relevant. Ten of these articles considered mid-infrared spectroscopy, three dealt with Raman spectroscopy and one with near-infrared spectroscopy. The metrics for detecting fibrosis were Pearson correlation coefficients ranging from 0.65-0.98; sensitivity from 76-100%; specificity from 90-99%; area under receiver operator curves from 0.83-0.98; and accuracy of 86-99%. Vibrational spectroscopy identified fibrosis in myeloproliferative neoplasms in bone, cirrhotic and hepatocellular carcinoma in liver, end-stage heart failure in cardiac tissue and following laser ablation for acne in skin. It also identified interstitial fibrosis as a predictor of early renal transplant rejection in renal tissue. Vibrational spectroscopic techniques can therefore accurately identify fibrosis in a range of human tissues. Emerging data show that it can be used to quantify, classify and provide data about the nature of fibrosis with a high degree of accuracy with potential scope for point-of-care use.
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Affiliation(s)
- Varun J Sharma
- Brian F. Buxton Department of Cardiac and Thoracic Aortic Surgery, Austin Health, Heidelberg, Melbourne, Vic, Australia; Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Spectromix Laboratory, Melbourne, Vic, Australia
| | - Aashima Singh
- Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Melbourne Medical School, The University of Melbourne, Vic, Australia
| | | | - Jaishankar Raman
- Brian F. Buxton Department of Cardiac and Thoracic Aortic Surgery, Austin Health, Heidelberg, Melbourne, Vic, Australia; Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Spectromix Laboratory, Melbourne, Vic, Australia; Department of Cardiac Surgery, St Vincent's Hospital, Fitzroy, Melbourne, Vic, Australia.
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3
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Nazeer SS, Venkataraman RK, Jayasree RS, Bayry J. Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check. Anal Chem 2024; 96:957-965. [PMID: 38164878 DOI: 10.1021/acs.analchem.3c02590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.
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Affiliation(s)
- Shaiju S Nazeer
- Department of Chemistry, Indian Institute of Space Sciences and Technology, Thiruvananthapuram, Kerala 695547, India
| | - Ravi Kumar Venkataraman
- Ultrafast Laser Spectroscopy Lab, Center for Integrative Petroleum Research, King Fahd University of Petroleum and Minerals, Dhahran 31261, Kingdom of Saudi Arabia
| | - Ramapurath S Jayasree
- Division of Biophotonics and Imaging, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala 695012, India
| | - Jagadeesh Bayry
- Department of Biological Sciences and Engineering, Indian Institute of Technology Palakkad, Palakkad 678623, India
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4
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Holcombe B, Foes A, Banerjee S, Yeh K, Wang SHJ, Bhargava R, Ghosh A. Intermediate Antiparallel β Structure in Amyloid β Plaques Revealed by Infrared Spectroscopic Imaging. ACS Chem Neurosci 2023; 14:3794-3803. [PMID: 37800883 PMCID: PMC10662787 DOI: 10.1021/acschemneuro.3c00400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023] Open
Abstract
Aggregation of amyloid β (Aβ) peptides into extracellular plaques is a hallmark of the molecular pathology of Alzheimer's disease (AD). Amyloid aggregates have been extensively studied in vitro, and it is well-known that mature amyloid fibrils contain an ordered parallel β structure. The structural evolution from unaggregated peptide to fibrils can be mediated through intermediate structures that deviate significantly from mature fibrils, such as antiparallel β-sheets. However, it is currently unknown if these intermediate structures exist in plaques, which limits the translation of findings from in vitro structural characterizations of amyloid aggregates to AD. This arises from the inability to extend common structural biology techniques to ex vivo tissue measurements. Here we report the use of infrared (IR) imaging, wherein we can spatially localize plaques and probe their protein structural distributions with the molecular sensitivity of IR spectroscopy. Analyzing individual plaques in AD tissues, we demonstrate that fibrillar amyloid plaques exhibit antiparallel β-sheet signatures, thus providing a direct connection between in vitro structures and amyloid aggregates in the AD brain. We further validate results with IR imaging of in vitro aggregates and show that the antiparallel β-sheet structure is a distinct structural facet of amyloid fibrils.
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Affiliation(s)
- Brooke Holcombe
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, AL 35401, USA
| | - Abigail Foes
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, AL 35401, USA
| | - Siddhartha Banerjee
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, AL 35401, USA
| | - Kevin Yeh
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Shih-Hsiu J. Wang
- Departments of Pathology and Neurology, Duke University, Durham, NC 27710, USA
| | - Rohit Bhargava
- Departments of Bioengineering, Electrical and Computer Engineering, Mechanical Science and Engineering, Chemical and Biomolecular Engineering, and Chemistry, Beckman Institute for Advanced Science and Technology, Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Ayanjeet Ghosh
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, AL 35401, USA
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5
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Falahkheirkhah K, Mukherjee SS, Gupta S, Herrera-Hernandez L, McCarthy MR, Jimenez RE, Cheville JC, Bhargava R. Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning. CANCER RESEARCH COMMUNICATIONS 2023; 3:1875-1887. [PMID: 37772992 PMCID: PMC10506535 DOI: 10.1158/2767-9764.crc-23-0226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023]
Abstract
Histopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend the computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalin-fixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows. SIGNIFICANCE Archival-quality (formalin-fixed paraffin-embedded), thin-section diagnostic images are obtained from thick-cut, fresh-frozen prostate tissues without dyes or stains to expedite cancer histopathology by combining SRS microscopy and machine learning.
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Affiliation(s)
- Kianoush Falahkheirkhah
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois
| | - Sudipta S. Mukherjee
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
| | - Sounak Gupta
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Rafael E. Jimenez
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - John C. Cheville
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Rohit Bhargava
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois
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6
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Yeh K, Sharma I, Falahkheirkhah K, Confer MP, Orr AC, Liu YT, Phal Y, Ho RJ, Mehta M, Bhargava A, Mei W, Cheng G, Cheville JC, Bhargava R. Infrared spectroscopic laser scanning confocal microscopy for whole-slide chemical imaging. Nat Commun 2023; 14:5215. [PMID: 37626026 PMCID: PMC10457288 DOI: 10.1038/s41467-023-40740-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Chemical imaging, especially mid-infrared spectroscopic microscopy, enables label-free biomedical analyses while achieving expansive molecular sensitivity. However, its slow speed and poor image quality impede widespread adoption. We present a microscope that provides high-throughput recording, low noise, and high spatial resolution where the bottom-up design of its optical train facilitates dual-axis galvo laser scanning of a diffraction-limited focal point over large areas using custom, compound, infinity-corrected refractive objectives. We demonstrate whole-slide, speckle-free imaging in ~3 min per discrete wavelength at 10× magnification (2 μm/pixel) and high-resolution capability with its 20× counterpart (1 μm/pixel), both offering spatial quality at theoretical limits while maintaining high signal-to-noise ratios (>100:1). The data quality enables applications of modern machine learning and capabilities not previously feasible - 3D reconstructions using serial sections, comprehensive assessments of whole model organisms, and histological assessments of disease in time comparable to clinical workflows. Distinct from conventional approaches that focus on morphological investigations or immunostaining techniques, this development makes label-free imaging of minimally processed tissue practical.
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Affiliation(s)
- Kevin Yeh
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Ishaan Sharma
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Kianoush Falahkheirkhah
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Matthew P Confer
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Andres C Orr
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yen-Ting Liu
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Yamuna Phal
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Ruo-Jing Ho
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Manu Mehta
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Ankita Bhargava
- University of Illinois Laboratory High School, Urbana, IL, 61801, USA
| | - Wenyan Mei
- Department of Comparative Biosciences, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, 61802, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Georgina Cheng
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Carle Health, Urbana, IL, 61801, USA
| | - John C Cheville
- Department of Laboratory Medicine and Pathology, College of Medicine and Science, Mayo Clinic, Rochester, MN, 55905, USA
| | - Rohit Bhargava
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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7
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Mukherjee SS, Bhargava R. Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets. Anal Chem 2023; 95:11365-11374. [PMID: 37458316 PMCID: PMC11060876 DOI: 10.1021/acs.analchem.3c01539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Infrared (IR) spectroscopic imaging is potentially useful for digital histopathology as it provides spatially resolved molecular absorption spectra, which can subsequently yield useful information by powerful artificial intelligence methods. A typical analysis pipeline in using IR imaging data for chemical pathology often involves iterative processes of segmentation, evaluation, and analysis that necessitate rapid data exploration. Here, we present a fast, reliable, and intuitive method based on a phasor representation of spectra and discuss its unique applicability for IR imaging data. We simulate different features extant in IR spectra and discuss their influence on the phasor waveforms; similarly, we undertake IR image analysis in the transform space to understand spectral similarity and variance. We demonstrate the potential of phasor analysis for biomedical tissue imaging using a variety of samples, using fresh frozen surgical prostate resections and formalin-fixed paraffin-embedded breast cancer tissue microarray samples as model systems that span common histopathology practice. To demonstrate further generalizability of this approach, we apply the method to data from different experimental conditions─including standard (5.5 μm × 5.5 μm pixel size) and high-definition (1.1 μm × 1.1 μm pixel size) Fourier transform IR (FTIR) spectroscopic imaging using transmission and transflection modes. Quantitative segmentation results from our approach are compared to previous studies, showing good agreement and quick visualization. The presented method is rapid, easy to use, and highly capable of deciphering compositional differences, presenting a convenient tool for exploratory analysis of IR imaging data.
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Affiliation(s)
- Sudipta S Mukherjee
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Rohit Bhargava
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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